gmo corn experiment results

ORIGINAL RESEARCH article

Instance segmentation to estimate consumption of corn ears by wild animals for gmo preference tests.

• 1 Institute of Artificial Intelligence, University of Georgia, Athens, GA, United States
• 2 Bio-Sensing and Instrumentation Laboratory, College of Engineering, University of Georgia, Athens, GA, United States
• 3 Biology Fortified, Inc., Middleton, WI, United States
• 4 Horticulture Section, School of Integrative Plant Science, Cornell AgriTech, Cornell University, Geneva, NY, United States
• 5 Phenomics and Plant Robotics Center, University of Georgia, Athens, GA, United States

The Genetically Modified (GMO) Corn Experiment was performed to test the hypothesis that wild animals prefer Non-GMO corn and avoid eating GMO corn, which resulted in the collection of complex image data of consumed corn ears. This study develops a deep learning-based image processing pipeline that aims to estimate the consumption of corn by identifying corn and its bare cob from these images, which will aid in testing the hypothesis in the GMO Corn Experiment. Ablation uses mask regional convolutional neural network (Mask R-CNN) for instance segmentation. Based on image data annotation, two approaches for segmentation were discussed: identifying whole corn ears and bare cob parts with and without corn kernels. The Mask R-CNN model was trained for both approaches and segmentation results were compared. Out of the two, the latter approach, i.e., without the kernel, was chosen to estimate the corn consumption because of its superior segmentation performance and estimation accuracy. Ablation experiments were performed with the latter approach to obtain the best model with the available data. The estimation results of these models were included and compared with manually labeled test data with R 2 = 0.99 which showed that use of the Mask R-CNN model to estimate corn consumption provides highly accurate results, thus, allowing it to be used further on all collected data and help test the hypothesis of the GMO Corn Experiment. These approaches may also be applied to other plant phenotyping tasks (e.g., yield estimation and plant stress quantification) that require instance segmentation.

1 Introduction

Corn is one of the world’s most important crops and is produced both traditionally and with genetically modified organisms (GMO) ( FAOSTAT, 2018 ). To obtain certain agriculturally desirable traits, such as resistance to pests, herbicide tolerance, and drought tolerance, specific corn varieties have been genetically engineered. Despite research on its safety and equivalence to traditional varieties, questions continue to be raised by members of the public regarding its safety and edibility. Since its introduction, there have been mixed views on GMO foods and crops and GMO corn is no exception for this. An early study summarizes the environmental benefits and risks of GMO corn ( Gewin, 2003 ). In 2008, a United States (US) grower observed that mice preferred non-GMO corn over GMO corn ( Roseboro, 2008 ). To test this, another grower repeated the experiment and published his results online stating that “The squirrel could have switched to GMO, but it did not. It knew it was different” ( Roseboro, 2013 ). Another study done by an Italian group claimed GMO corn to be toxic ( Séralini et al., 2012 ). This led to several studies and debates amongst research community and the corn industry ( Butler, 2012 ). The initial study was retracted because of a lack of detailed analysis and insufficient evidences, but this did not stop further studies and debates on GMO corn. Further studies in favor and against GMO corn were done which is collectively reviewed by Chassy and Tribe (2010) and some of them clarified that “ animals are not biased to organic corn. ”

During this time, the hypothesis was formulated that wild animals, specifically squirrels and deer, can sense differences between GMO and non-GMO corn and avoid GMO corn or prefer non-GMO corn when given a choice between the two. To test this hypothesis, a nation-wide community science project called “The GMO Corn Experiment” was started in 2015 that gathered the image data of GMO and non-CMO corn set outside by volunteers in their yards ( Haro von Mogel and Bodnar, 2015 ). The next challenge of this experiment was to estimate the precise consumption of all the GMO and non-GMO corn samples based on the complex image data obtained to address the hypotheses being tested in the study. In the experiment, there were two choices of corn for animals kept side-by-side with bar-coded labels to keep volunteer community scientists blinded to the identity of the ears. On a larger scale, this study can serve as a model for studying animal preferences of GMO vs. non-GMO food in a publicly accessible manner. The experiment recruited volunteers ranging from families with children, to school classrooms, and adults, which necessitated striking a balance between simplicity and thoroughness in the data collection strategy. Volunteers were asked to take images of one side of the ears of corn before and after 24 h, but had the option to add additional data and observations. This helped provide consistent samples of each ear of corn, however, the image data still presented challenges to analyze. Visual observations of the ears could be unreliable, as well as time-consuming and tedious to accurately estimate the consumption of each ear used in the experiment due to the large number of images and the time required for each image. Multiple observers would be needed to overcome individual biases, and any error could result in supporting a false hypothesis and affect future research. Finally, the wide range of image orientations, dimensions, quality, and lighting conditions would make traditional computer analyses difficult to perform, so a more robust method of analyzing community science-generated images was needed.

Computer vision can play an important role in estimating the consumption rate automatically. One of the challenges is to identify corn ears from the images and distinguish between the consumed and the non-consumed parts of the corn. Thus, a detection algorithm is needed that will distinguish between different parts of the corn, and based on its detection, compute the consumption percentage. Object detection and instance segmentation are two common categories in computer vision used to detect, classify, and segment images based on predefined/labeled classes. In object detection, the object’s location in the given image is identified, whereas instance segmentation detects and delineates each distinct object of interest with the corresponding pixels in the image. There have been many studies on segmentation of plants/crops to detect different diseases and various image processing techniques have been reviewed by Hamuda et al. (2016) . For instance, one study used color transformation from the RGB to CIELAB color space to segment blight in corn leaves ( Sukmana and Rahmanti, 2017 ) while another study used color features and K-means clustering to segment and identify crop diseases ( Kumar and Jayasankar, 2019 ). Compared to traditional image processing, deep learning has shown promising results in fruit detection, plant phenotyping, and yield estimation tasks ( Koirala et al., 2019 ; Jiang and Li, 2020 ). For example, one study proposed a method to augment training images using synthetic data to train a deep learning model Mask R-CNN to segment individual leaves of a plant ( Ward et al., 2018 ).

Image instance segmentation was best suited to estimate the consumption of corn because it is crucial to know the exact area of the individual ear of corn and the consumed part of the corn. The problem is with the subset of instance segmentation, in which there may be more than one label for single pixel, a phenomenon known as multi-label segmentation. This poses challenges as most current algorithms cannot handle this type of task. Recent advances in Convolutional Neural Networks led to a variety of frameworks that can be used to perform instance segmentation on different levels ( Hafiz and Bhat, 2020 ). One of the most successful and popular approach was the Mask R-CNN framework which efficiently detects the object while simultaneously generating a high quality segmentation mask for each instance ( He et al., 2017 ). It achieved an average precision (AP) of 37.1% with a speed of five frames per second on benchmark datasets. Moreover, Mask R-CNN has proven efficient in segmenting leaves ( Ward et al., 2018 ) and nuclei ( Johnson, 2018 ) which relied upon a limited amount of data for training. In this paper, an automated algorithm is proposed that uses the masks produced by the Mask R-CNN method to estimate the area of the consumed part of the corn as well as the entire corn, which results in a percentage consumption/eaten. Specific objectives of this study were to:

1 Compare the data labeling approaches that were used for model training.

2 Perform ablation experiments for model training parameters and identify an optimal training data size.

3 Evaluate the model performance in segmentation using manually labeled ground truth.

2 Materials and Methods

The proposed workflow is presented in Figure 1 to estimate corn consumption from raw images collected. This involves three primary tasks: 1) Raw data labeling, 2) Training Mask R-CNN to segment corn instances, 3) Consumption estimation from segmentation results.

FIGURE 1 . An overview of stages of consumption estimation from raw images. Collected raw images are labeled manually and used to train the Mask R-CNN model. The instances segmented by the model and their masks are then processed to estimate the consumption.

2.1 Image Collection and Annotation

2.1.1 data source and pre-screening.

Experiment kits were distributed to volunteer community (non-academic) scientists in the United States containing two experiments, each consisting of a pair of size-matched GMO and non-GMO corn, a feeding stand, and instructions for conducting the experiments. They placed experimental setups in their backyard or some kind of open space, thereby offering one GMO and one non-GMO corn to wild animals in the same environmental conditions, taking observations at 24 h intervals. Each ear of corn was labeled with an unique bar code without cultivar information to avoid potential human bias. A total of 630 images were provided at the start of the project, which needed to be analyzed in a reliable and repeatable manner. Based on visual observations, pairs of before/after images from each experiment were pre-screened, and if both of the ears of corn in the experiment were consumed between 0–5%, 5–95% or 95–100%, that image was placed in its respective category. In the case of mixed consumption, they were labeled as 5–95%. This approach allowed for the consideration of the potential variations in consumption for training the segmentation model. Table 1 shows the category-wise statistics of the images considered for further annotation stage.

TABLE 1 . Image categorization after pre-screening.

Multiple images were gathered from a single kit over the course of 24 h intervals, (e.g. the same corn ear would be consumed more on the second day than on the first day). For training purposes, images of the same ears of corn at different phases of the experiment were considered (initial, intermediate, final consumed image). This allowed more data to be collected with fewer experiment kits. After collecting the raw images in various conditions, a total of 450 images were selected from these categories for manual annotation and labeling. In this dataset, ambiguous images are those in which it could not be identified whether the corn is present or not, and if present, consumption could not be estimated. Based on these considerations, images for training was selected based on following criteria:

• Corn ears present in the image should be identified easily by the human eye.

• The image should be high-resolution and have legible brightness and contrast.

• The skewing of the image, (i.e. rotating the image at various angles, changing the brightness/contrast, applying blur) should not result in ambiguous image.

• The image can contain other objects than corn such as a chair, table, person, toy, etc.

2.1.2 Labeling Approaches

The sorted image data were then labeled using VGG’s Image Annotator (VIA) tool ( Dutta et al., 2016 ). For this study the whole corn in the image as well as the consumed parts of the corn were needed to be identified. To achieve this, the masks were labeled in many different ways. However, predicting exact and adequate segments posed the biggest challenge to the estimates. The output of the model is based on the segments and class masks given for training. To estimate consumption, it is possible to compare the eaten part, i.e., the bare part of corn ear, with the total visible part of the corn ear. Also, an individual corn kernel or cluster of kernels might have been considered as separate classes. Two of many possible approaches that consider two distinct classes were attempted.

First, the whole corn and bare corn ear were considered as two distinct classes (Approach 1), while in the second approach, the clusters of intact corn kernels along with bare corn ears were considered as two separate classes to segment (Approach 2). The two approaches differ at the image labeling level. For Approach 1, masks were drawn for whole corn ears and bare cob parts were used for consumption estimation, while in Approach 2, masks were drawn for corn kernels and bare cob parts, the sum of which equals a whole visible corn ear (which is not considered as a separate class for segmentation but computed later by adding these two classes). Figure 2 illustrates sample images from the training dataset for both approaches.

FIGURE 2 . Illustration of two data labeling approaches. Representative images with different levels of consumption and corresponding masks. In Approach 1 (A–B) , the visible part of a whole corn ear and distinguishable bare part segments are labeled as two mask classes. In Approach 2 (C–D) , intact corn kernels and bare part segments are labeled as two mask classes.

After performing the experiments with these approaches, which are discussed in later sections of this paper, manual labeling of all 450 images was continued for Approach 1. Table 2 provides the details of data partitions and the corresponding number of labeled masks for Approach 1. It should be noted that a normal image in the dataset has two corn ears. The table shows that there are certain images present in the dataset, which contains only one corn ear. These images may not be useful for comparing two corn ears, but they were selected to improve image segmentation accuracy and to avoid overfitting the model to specific features in images having two corn ears.

TABLE 2 . Data partitioning and number of masks for labeling approach 1.

2.2 Corn Segmentation

2.2.1 mask r-cnn.

As the Mask R-CNN architecture has proven successful in a wide range of applications requiring instance segmentation, it was chosen for the present study. It consists of two stages: the first stage scans the image and generates areas with a high probability of containing an object of interest, often referred to as proposals , and the second stage is responsible for the classification of these proposals to generate bounding boxes and masks for each detected object. To build the basic network, we used Matterport’s ( Abdulla, 2017 ) implementation of Mask R-CNN and performed ablation experiments with modified configurations.

Based on experiments with this implementation for different segmentation tasks such as deep leaf segmentation ( Ward et al., 2018 ) and nuclei segmentation ( Johnson, 2018 ; Naylor et al., 2018 ), widely used common parameters were chosen for the training. Also, ResNet-50 was ( He et al., 2016 ) as the backbone network to detect features. To improve standard feature extraction, a feature pyramid network (FPN) ( Lin et al., 2017 ) was added to the network. Preliminary trials with small samples were conducted to tune the hyper-parameters such as learning rate, non-max suppression threshold, and training ROIs per image. The remaining parameters were left unchanged from Matterport’s original implementation. The training followed a predetermined backpropagation schedule with a stochastic gradient descent (SGD) optimizer, L2 Regularization with a weight decay of 0.0001, a learning rate of 0.001, and cross entropy loss functions for various losses in the network.

During the preliminary trials, the model tended to overfit because of a small sample size of 50, as the model learned the features only specific to those training images, such as the number of corn ears present and the position and alignment of corn ears in the picture. Therefore, to prevent model overfitting, image augmentation ( Jung, 2018 ) methods were used to enhance the dataset diversity. These augmentations include random image flips (left/right/up/down) and rotations (90°, 180°, 270°) along with Gaussian blur, color multipliers. The gaussian blur and color multiplier takes care of variations in the dataset such as image focus, distance (pan/zoom) of object from camera, color variations in objects as well as background. Also, to avoid having the images with similar test-kit positions i.e., two vertical ears in the center of the image, we considered the images with skewed ear positions, various image angles and kits having only one corn ear in the training dataset.

The dataset has images of different resolutions ranging from 640 × 480 to 4,080 × 3,072 with an average of 1,280 × 760. For training, the input image size was limited to 1,024 × 1,024 with the help of Matterport’s utility methods that uses the standard bilinear interpolation to resize the image. A batch size of two was used because of the GPU memory limit (NVIDIA GeForce GTX1080Ti) and generally, steps per epoch are decided by batch size along with number of training samples. In this work, training configurations were optimized by observing the differences between training and learning methods and to observe which one can perform better in terms of estimating the corn consumption.

2.2.2 Ablation Experiments

To begin the consumption estimation, a better segmentation model was trained by performing various ablation experiments, which led to the effective end model used for testing. Labeling approach and training sample size were the two primary factors considered in the ablation experiments.

1) Labeling Approach comparison. The overall segmentation problem was simplified at the data labeling level. All the raw images were labeled according to one of the two approaches explained in the previous section. Instead of labeling the entire dataset twice to arrive at a better labeling approach for further experiments, this test was performed at the beginning with a smaller dataset. For initial comparison purposes, out of the 450 raw images, 70 were selected and manually labeled using both approaches. For this experiment, 50 training images and 20 validation images were used. Identical network configurations were selected for both labeling methods. This experiment indicated which labeling approach to follow for the remaining raw images.

Labeling each image for the two approaches took considerable time. In a standard image with two corn ears, both class instances could be labeled in one image within 3 min on average using Approach 1, while Approach 2 took 4.5 min on average. This was because, in a single image total instances of corn kernels can be more than total whole corn instances. There will be at most two whole corn instances but can be zero or multiple corn kernels present in one image, labeling multiple corn kernel instances contributed to more time in Approach 2. In the end, the Mask R-CNN model trained using images labeled by approach 1 were referred to as Model one while the one by approach 2 as Model 2 .

2) Training sample size effect. One of the challenges in training effective deep learning models is the limited amount of training data. The sparsity of labeled images in the agricultural domain is a common problem for segmentation model failures resulting from overfitting to a small sample size. The number of training images required is not fixed, but they are domain and application specific. To ascertain the minimum number of training images for a good segmentation performance, this experiment was performed.

To answer this question, multiple models were trained with a different number of training images with an approach selected from the above comparison. Starting with 50 images, models were trained on increments of 50 images, up to 300 images, while keeping an uniform size in the validation dataset. In preliminary tests, it was observed that, when the selected training samples contained only the images from a certain category, (e.g. “95–100% consumed”), the resultant model performed poorly on the remaining categories, which resulted in inaccurate image segmentation. To address this data imbalance, each training procedure was performed five times by selecting the training images randomly from each category. Thus, a total of 30 different segmentation models were trained with six different sample sizes.

Apart from the major experiments mentioned above, the transfer learning phenomenon applied to this use case was also examined. During preliminary testing, one of the Mask R-CNN model was trained by initializing random weights at the beginning. Then, this model was compared to a model trained on pre-trained weights on the MS-COCO dataset ( Lin et al., 2014 ). For better segmentation of the background from the corn ears in the image, further models were trained using COCO initial weights.

2.2.3 Evaluation Metrics

To evaluate the performance of the above ablation experiments, a comparison was made of the training procedures as well as the results on labeled test dataset. Below are the metrics considered for evaluating these experiments.

1) Jaccard Index. It is important to predict the mask accurately as the area to be calculated is based on the mask. This can be verified by standard metrics of Jaccard Index, a.k.a. Intersection over Union (IoU), which is the ratio between the overlap of a predicted mask and the actual ground truth mask and the area of union between the predicted mask and the ground truth mask. Then the weighted mean IoU was computed for all the predicted instances of each class in an image and average all the images in the dataset. In both models, class “ bare cob ” is present, while Model 1 has “ whole corn ” and Model 2 has “ corn kernel ” as the second class, respectively. Mean IoU will be a key metric used to evaluate the segmentation performance of these models.

2) Mean Average Precision. For instance segmentation, it is important to compute the precision and recall achieved by the model in addition to IoU. Precision is defined as a ratio of true positives over both true and false positives. Recall is defined as the ratio of true positives over both true positives and false negatives. Precision and recall are computed over a range of different IoU thresholds (typically 0.5 to 0.95 in steps of 0.05). The average precision (AP) is the averaged precision for all classes for one input image. The mean of APs is the mean average precision (mAP) over all images.

3) PR Curve. Along with precision values, the recall of these models can be visualized better in terms of a precision-recall (PR) Curve. The area under the PR curve for a certain IoU threshold is nothing but the mAP for that model. We can plot the PR curve for different IoU threshold values that are especially close to the model’s mean IoU to ascertain how well it is performing on all of the ground truth instances.

The main criteria for selection of a better labeling approach involves a high mean IoU value, a near ideal PR curve for different IoU thresholds, and thus a high mAP value. Additionally, it is important to consider the complexity in computing the consumption ratio with segmentation masks of two different labeling approaches. The ideal approach should be less complex in terms of detection of instances. It should present a significant overlap of predictions and ground truth masks.

2.3 Corn Consumption Estimation and Comparison

2.3.1 calculation of corn consumption ratio.

Once a network was trained to segment the required classes, distinct corn ears in the image would need to be identified to estimate the consumption. For this, a straightforward method was used for preparing a list of all distinct class instances and compute the sum of individual pixels. The detection results of the model provides all instances of a whole corn, the consumed part of the corn, and corn kernels (in Approach 2). Each instance has its mask pixels and the bounding box coordinates. This output was further processed using custom Python scripts to arrive at final consumption estimations.

After segmenting the image into the whole corn and its corresponding parts—bare cob part and corn kernels (as illustrated in Figure 1 ), the next step is to group the segmentation results to map all parts to respective corn ears. This is done by using the bounding box created by Mask R-CNN’s bounding box detection layer. For example in Approach 1, the individual whole corn instances are first separated and then grouped among all other segmented instances of bare cob parts in their bounding box. This provides mapping of all whole corn instances and their bare cob parts.

Finally, the consumption was determined by calculating the ratio of total pixels of all individual consumed or bare cob parts of the corn over total pixels of that corn (In Approach 2, the total pixels of corn is the sum of the pixels of all bare cob parts and the pixels of all kernel parts).

2.3.2 Evaluation Methods

During the pre-screening and categorizing stage, the images were manually annotated by authors. To verify the consumption value obtained from manual annotations, five human observers rated the test images. The averages of all observed consumptions were verified with the values obtained from manual annotations. In this comparison, the manual annotations were considered as ground truth for consumption estimation.

The consumption was calculated individually for each segmented corn ear. It should be noted that a variation of ±4% in consumption estimation was observed because of configurations of the test environment. This can be visualized by a scatter plot with a linearly fitted line. We observed failure in segmentation for a few of the models trained on less samples, which led to inaccurate consumption estimations. These cases were treated as outliers while evaluating consumption estimations. The outliers were not considered in fitting the line. The consumption of left and right corn ears was then compared, and the results can be viewed in the confusion matrix compared from human observations.

3 Results and Discussion

3.1 labeling approach comparison.

The metric values for the two annotation approaches were compared and the aim was to select one labeling approach that will be used for further experimentation and labeling the required training data. It can be seen that Model one gave better results on the overall test dataset ( Table 3 ). Figure 3 shows example outputs using the two approaches. Approach 2 missed a significant portion of both classes that can be seen by observing the instance masks. In addition, Approach 2 segmented the inner bare part as both classes due to its relatively small size and surrounding kernels, which was a false positive for corn kernel class and can result in an inaccurate estimation of consumption.

TABLE 3 . Labeling approach comparison.

FIGURE 3 . Segmentation using two labeling approaches. For better visualization masks are not shown for the whole corn class in Approach 1 (left) , while both class masks are shown in Approach 2 (right) .

Overall, labeling Approach 1 led to a better performance than Approach 2 which can be seen from above results. Compared with Approach 2, Approach 1 increased the segmentation accuracy by approximately 10% and 23% for bare cob and whole corn, respectively, which were substantial improvements for a two-class segmentation problem. This occurred primarily because segmentation of bare cob and whole corn was simpler than that of bare cob and corn kernels. First, a whole corn ear had a relatively predictable conical shape regardless of how much of it is consumed, whereas corn kernel parts could be in any shapes and locations based on the consumption of the ear. Given the same number of training images for a whole corn ear, a Mask R-CNN model could easily learn adequate feature representations, resulting in better segmentation accuracy. Second, Mask R-CNN could not achieve a perfect segmentation of objects with complex boundaries, such as bare cob and corn kernel parts both of which had variable instance mask boundaries that were not as obvious as the conical whole corn. In particular, the boundaries of the two parts were dramatically variable because of natural uncertainties in the experiments such as ear placement height and ear size. Additionally, since it was not possible to predict how the corn ears were consumed by animals and thus the remaining bare parts and kernel parts could vary. Approach 2 included both classes (bare cob and corn kernels) and increased the difficulty of training a model for such a segmentation task. In addition, the two annotation methods had different labeling cost and model training time.

Proceeding with Approach 1, it was determined that the consumption estimation was faster for all the different image types because of the easy computation involved in detecting the whole corn area. Approach 2 required extra computations to identify the appropriate kernels and bare parts for a single corn based on its bounding box. This was not the case with Approach 1, since it gives the bounding box of a whole corn that already has the bare part. Thus for initial experimentation purposes, Approach 1 performed considerably well.

3.2 Effect of Sample Size

Various models with different training sample sizes were compared in terms of key performance metrics ( Table 4 ). As more images were added to training, the segmentation improved generally. For bare cob class, the model with a sample size of 150 slightly under performed compared to model with 100 samples and thus lowering the total mAP by 0.014. With two-sided t-tests ( Supplementary Table S1–S3 ), the performance metrics were statistically analyzed to find the significant improvements corresponding to various sample sizes. Sample increment in most of the early sample sizes found to be insignificant with respect to IoU. However, we achieved significant improvement in mAP with all 300 images. Therefore, the use of all 300 training images is beneficial to the best performance in the present study. In the future, annotating 50 to 150 images would initiate a good baseline model at an affordable cost. With active learning methods, additional instances important to model performance improvements could be identified and labeled with minimized human efforts.

TABLE 4 . Effect of training sample size on segmentation performance (mean ± standard deviation).

Drawing the PR curves using these models can provide a more informative comparison of the segmentation performance of the models. Figure 4 shows the PR curve at higher IoU threshold values than the one used to obtain the mean IoU for both the classes shown in the above table. This was specifically done to observe the model performance in case a high IoU thresholds are considered at the time of segmentation. A model with 100 images can be considered as poor compared to a model with 300 training images, but models with 250 and 300 images performs nearly identical to each other in the segmentation task.

FIGURE 4 . Precision-recall (PR) curves. These are standard PR curves with IoU thresholds and corresponding AP values. As we increase the threshold, the area under the curve decreases, and after a certain number of training images, there is less variance.

From these observations, intuitively it is possible to answer the question of the number of images required for training. For segmentation of the dataset, all of the considered metrics showed that there was little difference in performance from 250 images to 300 images. There is no standard (fixed) ratio for selecting this number in most of the domains and thus we find the sufficient training size by performing these experiments. Hence, it can be said that, about 200–300 images are required for adequate segmentation in this case. Figure 5 shows some of the segmentation outputs from the test dataset. It can be seen that this model identified the bare parts very accurately, which is the key for calculating the consumption of corn ears.

FIGURE 5 . Segmentation results. Representative examples from test data with predicted masks.

3.3 Corn Consumption Estimation

The consumption values of 50 individual corn ears were obtained from the segmentation results of 25 test images and comparison was done with the average of human labeled ground truth values. The test images were annotated with Approach 1 and the consumption was calculated using these manually labeled masks. The results of this comparison showed that all of these models performed well on the test dataset but the models trained with lower number of images had a few outliers ( Figure 6 ). The best R 2 value of 0.9929 was achieved with models trained on 300 samples. There was not much difference in consumption calculations with these models, but it can be seen that there were more instances of failure in segmentation for the models trained with fewer images.

FIGURE 6 . Scatter plots of predicted vs. ground truth values of consumption ratio using models with different number of training images. The outliers shown in red are estimations when the model has failed to predict the class instance mask and they were not considered for computing the R 2 values.

3.3.1 Outlier Analysis

When the training set was smaller in size, there is a missed segmentation of bare cob with Approach 1 . Figure 7 shows the two images in which the right corn ear was not identified as Whole corn and Bare cob (which are the two classes we have considered). As per Approach 1, these corn ears belong to both the categories but the models with fewer training samples could identify that corn ear in only one of the classes, i.e., whole corn, because of which the predicted consumption is zero. As the segmentation itself failed, this is an outlier in consumption estimation and thus omitted from the scatter plots discussed above.

FIGURE 7 . Failed segmentation examples. The right ears in both images were completely consumed, but the models failed to identify it as both whole corn and bare cob part.

It can be seen that the right side corn ear in both images has a similar shaped structure in the background—a tree stem attached directly to the corn ear. This could be one of the reasons it is ambiguous at the pixel level. Also, these test images belong to 95–100% consumed category, for which we did not have abundant training data. In the total of 300 training images, we only had 10% of such images, and may be fewer than this when we randomly select a smaller sample size (50–150). This imbalanced training data can be fixed by adding more of such training samples, as shown in the model trained with 300 images, where we did not observe outliers.

3.3.2 Left-Right Comparison

The GMO and Non-GMO ears were randomly placed by participants on each feeding stand, and the identity of both corn ears in each image was blinded. To perform an unbiased comparison between ears, the model’s output was compared for the consumption of left and right corn ears within each image. The 25 test images were then classified in three categories—right, left, and equal—representing on which side the corn consumed more. The results can be seen in the confusion matrix—among the 25 test images, 22 classified images matched with the ground truth ( Figure 8 ). It should be noted that the remaining three images were misclassified by marginal differences between ground truth and predicted consumption estimation. For example, the image with equal consumption had both the corn ears 100% eaten, while the predicted consumption was 99% and 98% for the right and left ear respectively.

FIGURE 8 . Confusion matrix for left-right corn ear consumption comparison. In ground truth, there were 13 images in which the right ear is eaten more, one image in which both are equally consumed (as shown in Figures 7 and 11) images in which left ear is consumed more.

3.4 Segmentation of Ambiguous Data

As stated in section 2.1.1, the images that fits certain criteria were used to prepare the models discussed so far. Furthermore, the images in this actual case can have various abnormalities, such as varying light exposure, ambiguous corn ears, varying backgrounds, among others. To verify the performance of this model, a manually generated set of 20 images having corn ears in varying conditions was used and performed consumption estimations of these images. When the consumption of the same set of corn was computed from images taken from different perspectives, it was observed that there was a greater difference between the predicted consumption value and the ground truth. The R 2 value achieved was 0.88 for this dataset with more than 10 outliers.

Representative segmentation results illustrate that the effect of change in perspective as well as effect of brightness on segmentation ( Figure 9 ). The top two images are from the same corn ear set, but in the second row, we can see that the model failed to segment the right side corn due to its low resolution of the actual corn ear (the corn ears only take a very small portion of the whole image). On the other hand, the bottom two rows shows that if an image is taken from an appropriate distance, the model performs well in different conditions of light. Though the training data do not contain such images, the model can still correctly segment these instances and the performance of the models can be improved further by adding more of such images in training.

FIGURE 9 . Effect of background and resolution change on segmentation. Left column shows the original image while right shows the corresponding masks. Top two rows are the same set of ears and bottom two rows are the same, but with different background and resolutions.

3.5 Discussion

Since the inception of this project in 2018, a variety of new instance segmentation frameworks have been proposed as surveyed by Hafiz and Bhat (2020) . For example, Mask Scoring RCNN that also makes segmentation based on detection of region proposals extends further with the inclusion of mask overlap scores and has slightly surpassed Mask R-CNN to achieve 39.6% AP on the same datasets ( Huang et al., 2019 ). Another region proposal-based Path Aggregation Network–PANet achieved top performance in segmentation that enhances feature hierarchy by path augmentation ( Liu et al., 2018 ). Although the above mentioned frameworks perform better in terms of accuracy, the speed of detection remains an issue when the real time segmentation is to be performed. YOLACT ( Bolya et al., 2019a ) and YOLACT++ ( Bolya et al., 2019b ) addresses the segmentation speed at the cost of a reduction in AP by prototyping the masks and producing the instance masks with previously predicted mask coefficients. Most recent methods SOLO ( Wang et al., 2020a ) and SOLOv2 ( Wang et al., 2020b ) that addresses both speed and AP provides a simple, fast yet strong segmentation framework. This framework follows a rather unconventional approach to assign each pixel a “instance-category” to modify segmentation into a classification-solvable problem. To explore other segmentation approaches that are more recent and are lighter than Mask R-CNN, we performed experiments on available datasets using SoloV2 ( Supplementary Table S4 ). It was observed that SoloV2 segments the Whole Corn class with good precision but does not perform well for the Bare cob class. The result suggests that Mask R-CNN still remains to be a robust instance segmentation method and it performed satisfactorily with a small amount of data. In future studies, however, other newer instance segmentation models could be further explored to improve the segmentation performance.

In this study, the proposed instance image segmentation approach measured how much corn was removed from an ear by comparing bare ear to kernels. The same method could be used to measure yield losses where ears of corn are damaged by hail or partially eaten by pests and wild animals. If further developed by providing appropriate training data, the trained deep learning model could learn to identify particular types of damage. In participatory breeding, some field trials are conducted at remote locations and there is the challenge of measuring phenotypes that would otherwise be easy to measure with people and equipment available at one’s home institution ( Ceccarelli, 2015 ). The method developed in this study would allow farmers and others who are monitoring remote locations to be able to collect images that could be turned into useful data after the images are analyzed by similar deep learning models presented in this study with domain adaptation. A tool could be developed from this study that would allow farmers to take photos of damaged ears and quantify how much yield loss was caused by pests and disease during ear development ( Steinke et al., 2017 ). It should be noted that plant breeding is already incorporating machine learning approaches to analyze and predict phenotypes ( Singh et al., 2016 ; Jiang and Li, 2020 ), but the proposed approach is unique because it can utilize field-collected image data with varying angles, orientations, and lighting with non-standardized resolutions and uncontrolled background. Therefore, this approach can be useful for decentralized and participatory crop research and breeding.

4 Conclusions

In this work, a deep learning based framework to quantify the consumption of corn with a relatively small number of images collected by community scientists was presented and evaluated. The Mask R-CNN model was demonstrated to produce high quality results of pixel-wise segmentation for the challenging task of multi-label segmentation of consumed corn. The two approaches for labeling the ground truth were presented, and it was found that segmenting only the whole corn and its consumed part is sufficient for estimating consumption. The best results were obtained when the training data were sufficient and labeled with high accuracy. The effects of varying light conditions and background were examined and it was found that the Mask R-CNN model, which was not trained with such images, was able to identify certain segmentation instances accurately, and can be improved upon by including such images for further training. The framework developed in this study can be used to predict more samples collected in the GMO Corn Experiment and will produce reliable results more efficiently than manual labeling. Future work will be directed at improving the variation in accuracy as well as testing the visually challenging images, and toward applying the methods developed here along with additional lines of evidence to test the hypotheses that are the focus of the GMO Corn Experiment.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material and, further inquiries can be directed to the corresponding author.

Ethics Statement

Ethical review and approval was not required for the study involving human participants in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Institutional Animal Care & Use Committee, University of Florida.

Author Contributions

CL, SA, and YJ conceived the idea. KH provided corn ear images. SA implemented and trained the Mask R-CNN models, and performed all the experiments. CL and YJ verified the methods and experiments. CL directed and supervised the project. SA, YJ, KM, and CL wrote the initial draft. All authors reviewed and edited the draft.

This study was part of SA’s thesis that was supported partially by an internal research seed grant at the University of Georgia. Funding for the GMO Corn Experiment was provided by individual donations to Biology Fortified, Inc.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to acknowledge all the researchers, students, and community members who participated in collection of data for this project as well as the volunteers for providing visual ratings of consumption estimation. We would also like to acknowledge Monsanto Company for the donation of the ears used in the study, and also the many individual donors who supported our work.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2020.593622/full#supplementary-material .

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Keywords: deep learning, mask R-CNN, instance segmentation, GMO, image processing

Citation: Adke S, Haro von Mogel K, Jiang Y and Li C (2021) Instance Segmentation to Estimate Consumption of Corn Ears by Wild Animals for GMO Preference Tests. Front. Artif. Intell. 3:593622. doi: 10.3389/frai.2020.593622

Received: 11 August 2020; Accepted: 30 December 2020; Published: 29 January 2021.

Reviewed by:

Copyright © 2021 Adke, Haro Von Mogel, Jiang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Changying Li, Y3lsaUB1Z2EuZWR1

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Biofortified

Stronger Plants, Stronger Science, and Stronger Communication

GMO Corn Experiment ends June 27!

Finish your experiments before June 27, 2016 at midnight!

As of today, there are exactly twelve days left to complete the data collection for your GMO Corn Experiment kits. If you have one of these kits but have not yet completed your experiments, stop reading and go grab them right now! I’ll wait. Now that you’ve got the kit in front of you, register on our GMOExperiment.com website so you can enter your data. Follow the directions in the experimental protocol and get your first experiment set up and placed outside in a good place for animals to visit it. Take your starting picture of the experiment and sign into the website to upload your picture and record your observations. You’re already halfway done with the first experiment! It’s that easy. One day later, you can take your second picture, enter your data, and close out the experiment. Then, set up the second experiment and repeat! We already have more than 420 kits fully completed on the GMOExperiment.com website. This is fantastic! But the more data we can get – the better because it increases the statistical power of the experiment. This means that for each additional experiment you complete, the entire experiment gets that much better at determining whether wild animals can or cannot tell the difference between the GMO and non-GMO ears of corn. Your experiment kit can mean the difference between answering our research question to the satisfaction of the scientific community versus leaving it as an open question for people to debate. Let’s put the nail in the coffin on this question, and get those ears out on the nails of the your apparatus today! If you have any questions about your own experiment, registering, and setup, feel free to contact us at [email protected] and we will get back to you right away. Finally, make sure that when you enter your data on GMOExperiment.com that you fully close out your experiments. If you have not done so, please log in now and close them out.

What are the results?

Behind-the-scenes of the GMO Corn Experiment

Last call to get your name on a scientific paper!

Ok, now for my final pitch for our citizen scientists to get their experiments outside right away. Everyone who participates in this experiment will get their name on the final paper. That’s right, we are going to have one of the biggest acknowledgements section of any research paper on GMOs, and you will be able to have your name along with the donors, helpers, and everyone else who has been involved in this one-of-a-kind experiment. I can’t think of a better way to reward everyone for all their hard work than permanently recognizing their contributions in the scientific literature itself. That’s my final pitch – let’s get this done! (In case anyone wishes to get another experiment kit, while we can’t promise that there will be much time left to be included in the paper when you get it, we have a few left which you can get here .)

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Published by Karl Haro von Mogel

Karl Haro von Mogel serves as BFI’s Director of Science and Media and as Co-Executive Editor of the Biofortified Blog. He has a PhD in Plant Breeding and Plant Genetics from UW-Madison with a minor in Life Sciences Communication. He is currently a Postdoctoral Scholar researching citrus genetics at UC Riverside. View all posts by Karl Haro von Mogel

8 thoughts on “ GMO Corn Experiment ends June 27! ”

I recognized my experiment! (One of the ones pictured above…)

Fame and glory!

I should have sent the photo of the squirrel racing away with the GMO ear of corn in the yard. lol Alas, I wanted to be too ‘science-y’ about the experiment so sent the normal type photos.

I’d still love to see that if you have it–bring it over!

Actually, the squirrel stole the entire ear holder. Got a photo of the squirrel eating the corn still attached, where he stopped in the driveway.

Oh yeah, send that picture to us in our email, and we’ll add it to our collection of fun images!

I’ll have to check if I can find your email address.

What is the timeline for producing a final report on this?

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Science and History of GMOs and Other Food Modification Processes

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How has genetic engineering changed plant and animal breeding?

Did you know.

Genetic engineering is often used in combination with traditional breeding to produce the genetically engineered plant varieties on the market today.

For thousands of years, humans have been using traditional modification methods like selective breeding and cross-breeding to breed plants and animals with more desirable traits. For example, early farmers developed cross-breeding methods to grow corn with a range of colors, sizes, and uses. Today’s strawberries are a cross between a strawberry species native to North America and a strawberry species native to South America.

Most of the foods we eat today were created through traditional breeding methods. But changing plants and animals through traditional breeding can take a long time, and it is difficult to make very specific changes. After scientists developed genetic engineering in the 1970s, they were able to make similar changes in a more specific way and in a shorter amount of time.

A Timeline of Genetic Modification in Agriculture

A Timeline of Genetic Modification in Modern Agriculture

Circa 8000 BCE: Humans use traditional modification methods like selective breeding and cross-breeding to breed plants and animals with more desirable traits.

1866: Gregor Mendel, an Austrian monk, breeds two different types of peas and identifies the basic process of genetics.

1922: The first hybrid corn is produced and sold commercially.

1940: Plant breeders learn to use radiation or chemicals to randomly change an organism’s DNA.

1953: Building on the discoveries of chemist Rosalind Franklin, scientists James Watson and Francis Crick identify the structure of DNA.

1973: Biochemists Herbert Boyer and Stanley Cohen develop genetic engineering by inserting DNA from one bacteria into another.

1982: FDA approves the first consumer GMO product developed through genetic engineering: human insulin to treat diabetes.

1986: The federal government establishes the Coordinated Framework for the Regulation of Biotechnology. This policy describes how the U.S. Food and Drug Administration (FDA), U.S. Environmental Protection Agency (EPA), and U.S. Department of Agriculture (USDA) work together to regulate the safety of GMOs.

1992: FDA policy states that foods from GMO plants must meet the same requirements, including the same safety standards, as foods derived from traditionally bred plants.

1994: The first GMO produce created through genetic engineering—a GMO tomato—becomes available for sale after studies evaluated by federal agencies proved it to be as safe as traditionally bred tomatoes.

1990s: The first wave of GMO produce created through genetic engineering becomes available to consumers: summer squash, soybeans, cotton, corn, papayas, tomatoes, potatoes, and canola. Not all are still available for sale.

2003: The World Health Organization (WHO) and the Food and Agriculture Organization (FAO) of the United Nations develop international guidelines and standards to determine the safety of GMO foods.

2005: GMO alfalfa and sugar beets are available for sale in the United States.

2015: FDA approves an application for the first genetic modification in an animal for use as food, a genetically engineered salmon.

2016: Congress passes a law requiring labeling for some foods produced through genetic engineering and uses the term “bioengineered,” which will start to appear on some foods.

2017: GMO apples are available for sale in the U.S.

2019: FDA completes consultation on first food from a genome edited plant.

2020 : GMO pink pineapple is available to U.S. consumers.

2020 : Application for GalSafe pig was approved.

How are GMOs made?

“GMO” (genetically modified organism) has become the common term consumers and popular media use to describe foods that have been created through genetic engineering. Genetic engineering is a process that involves:

• Identifying the genetic information—or “gene”—that gives an organism (plant, animal, or microorganism) a desired trait
• Copying that information from the organism that has the trait
• Inserting that information into the DNA of another organism
• Then growing the new organism

How Are GMOs Made? Fact Sheet

Making a GMO Plant, Step by Step

The following example gives a general idea of the steps it takes to create a GMO plant. This example uses a type of insect-resistant corn called “Bt corn.” Keep in mind that the processes for creating a GMO plant, animal, or microorganism may be different.

To produce a GMO plant, scientists first identify what trait they want that plant to have, such as resistance to drought, herbicides, or insects. Then, they find an organism (plant, animal, or microorganism) that already has that trait within its genes. In this example, scientists wanted to create insect-resistant corn to reduce the need to spray pesticides. They identified a gene in a soil bacterium called Bacillus thuringiensis (Bt) , which produces a natural insecticide that has been in use for many years in traditional and organic agriculture.

After scientists find the gene with the desired trait, they copy that gene.

For Bt corn, they copied the gene in Bt that would provide the insect-resistance trait.

Next, scientists use tools to insert the gene into the DNA of the plant. By inserting the Bt gene into the DNA of the corn plant, scientists gave it the insect resistance trait.

This new trait does not change the other existing traits.

In the laboratory, scientists grow the new corn plant to ensure it has adopted the desired trait (insect resistance). If successful, scientists first grow and monitor the new corn plant (now called Bt corn because it contains a gene from Bacillus thuringiensis) in greenhouses and then in small field tests before moving it into larger field tests. GMO plants go through in-depth review and tests before they are ready to be sold to farmers.

The entire process of bringing a GMO plant to the marketplace takes several years.

Learn more about the process for creating genetically engineered microbes and genetically engineered animals .

What are the latest scientific advances in plant and animal breeding?

Scientists are developing new ways to create new varieties of crops and animals using a process called genome editing . These techniques can make changes more quickly and precisely than traditional breeding methods.

There are several genome editing tools, such as CRISPR . Scientists can use these newer genome editing tools to make crops more nutritious, drought tolerant, and resistant to insect pests and diseases.

Learn more about Genome Editing in Agricultural Biotechnology .

How GMOs Are Regulated in the United States

GMO Crops, Animal Food, and Beyond

How GMO Crops Impact Our World

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As Scientists Question New Rat Study, GMO Debate Rages On

Dan Charles

Italian farmer Giorgio Fidenato picks up what's left of his genetically altered corn after anti-GMO activists trampled it, back in 2010. Paolo Giovannini/AP hide caption

Italian farmer Giorgio Fidenato picks up what's left of his genetically altered corn after anti-GMO activists trampled it, back in 2010.

The headlines on the press releases that started showing up yesterday, here at The Salt certainly got our attention. Just one sample: " BREAKING NEWS: New Study Links Genetically Engineered Food to Tumors."

The reason for all the excitement was a study published this week in the well-respected journal Food and Chemical Toxicology. The French scientists who conducted the study basically concluded that rats fed a diet of genetically modified corn and small amounts of herbicides got sicker faster than their counterparts eating regular corn and no herbicides.

Based on the study, several anti-GMO groups are calling for a ban and the French government is calling for more investigation, but many scientists met the research with a heavy dose of skepticism.

The study describes an experiment in which scientists fed 18 different groups of rats (ten rats in each group) various concentrations of a genetically engineered kind of corn and/or small amounts of the herbicide Roundup. (Roundup is widely used in combination with genetically engineered "Roundup Ready" crops.)

Two control groups got a diet of non-modified corn and no Roundup. The experiment went on for two years, an unusually long time. Most experiments with rats that attempt to measure the toxic effects of chemicals last only 90 days.

On average, the rats that ate GM corn or drank water laced with Roundup did worse than the control group. Tumors showed up earlier (at least in some of the groups) and more rats died than in the control groups. The contrast was most stark among female rats.

So is this solid evidence that GM corn (or other crops) are bad for you? The author of the paper, Gilles-Eric Seralini , who has been campaigning against GM crops since 1997, says yes. Definitely yes.

Other scientists say, absolutely not.

Some of their complaints about the study are aimed at the study's methods. Critics point out that the type of experimental rats used in this study are particularly prone to tumors. So if you divide up 200 of them into twenty groups, as this study did, you are likely to get very high tumor rates in some of the groups. And the fact that such clusters of tumors didn't show up in the two small control groups could easily be due to random chance.

One particularly irreverent critic , Michael Grayer, a medical statistician, pointed out that the study included 18 groups of rats that were exposed to GMOs or Roundup (nine each for male and female rats), compared to only two control groups. "The potential for cherry-picking the nice positive results here from a sea of boring null ones is immense," he wrote on his blog. "Not saying they did it, of course, but it's certainly a concern."

Also, if this experiment truly showed a link between genetically engineered food and tumors, one might expect the rats that ate more of the GM corn to develop more tumors. In fact, the opposite happened. The rats eating a diet of 33 percent GMO corn stayed healthier than animals eating food with a GMO concentration of just 11 percent.

Seralini, for his part, says that this simply shows that GMOs are toxic in a different way. They merely need to rise above a certain threshold level to have harmful effects; increasing the concentration doesn't increase the harm.

Some scientists were inclined to dismiss the study simply based on Seralini's history of anti-GMO claims. "I know this guy. He has published a lot of rubbish," says Harry Kuiper , a Dutch scientist who used to be in charge of the European Commission's research program on the safety of genetically modified foods. That program sponsored many previous studies, including animal feeding studies, which came to less alarming conclusions and got much less attention than Seralini's.

In fact, Kuiper and many of his colleagues don't even think that animal feeding studies are a very good tool for studying the safety of GM foods. If there was a harmful chemical hidden in GM corn, they say, such studies wouldn't be likely to catch it, because rats can't eat enough corn for them to get a harmful dose of the toxic or cancer-causing substance.

But apart from methodological concerns and personal animosity, there's a deeper reason why scientists like Kuiper give little credence to Seralini's studies. There's a saying in science: Extraordinary claims require extraordinary evidence. For most of the scientists who have been studying the safety of GMOs, it's an extraordinary claim, at this point, to assert that the current generation of genetically modified crops are harmful to human health.

There's no apparent reason why that should be true; No one has found new toxic substances in these crops. And the giant feeding experiment that's been going on for the past fifteen years — hundreds of millions of Americans consuming GMO ingredients — hasn't produced evidence of harm, either.

It would take a lot more evidence that the results of this study to change their minds.

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The GMO Corn Experiment

• $13,523 pledged
• 218% funded
• Funded on 11/01/15

Experimental Protocol

This is the official experimental protocol for the GMO Corn Experiment.

Please read through the entire protocol before beginning any part of the protocol. If you have any questions, email [email protected] or call (608) 284-8842.

Assemble the Feeding Stand

• Take the wood plank and nails out of your experiment kit.
• Place both ends of the plank on a raised surface with at least four inches of space underneath so the nails do not damage anything when they are driven through the wood.
• Warning. Adults only. Wear gloves to avoid injury. With the pre-drilled holes as a guide, hammer the nails into the plank until they are flush with the surface of the wood.
• Turn the feeding stand over - it is ready to add the corn!
• Start with the bags marked with the number 1 for experiment #1. Once that experiment is complete, use the bags marked with the number 2 for experiment #2. Open only one ear bag at a time to avoid mixing up the ears before you put them on the stand.
• Some animals prefer ears that are already “open”, so remove a few kernels from the top of each ear if your ears do not have any missing kernels.
• Optional. If you have a kitchen scale, you can measure the weight of each ear before you put it on the feeding stand. Use the same scale for each weight and record the data in grams .
• Warning. Adults only. Wear gloves to avoid injury. Carefully and slowly place the base of one ear on a nail and gently push until the bottom of the ear is flush with the wood.
• Stick the barcode sticker on the side of the board by that ear so it will be visible in your photos. Place the sticker on the smoother side of the wood plank.
• Repeat with the second ear and barcode sticker from the same experiment.

Register for the GMO Experiment Website and Enter Starting Data

• Register at GMOexperiment.com by entering the kit number and all the ear barcode numbers in your kit. Log in and follow the instructions to enter your kit information.
• Click “Start Experiment 1” and follow the instructions to start the experiment.
• Change the ear positions on the website (left or right side) to match your feeding stand.
• Enter the type of location and expected animals for where you will place your feeding stand.
• Enter the street address or other location information for where you will place your feeding stand. Note that we will not share your address. Be as precise as possible. If there is no address, provide a detailed description of where you placed the feeding stand. Include a zip code if possible. You can use your smart phone to find the GPS coordinates (on iPhone, your latitude and longitude can be found in the Compass app).

Place the Feeding Stand

• Check the weather and conduct your experiment on a day where there is a low chance of rain. Wet corn can quickly grow mold containing toxins that can harm or even kill animals.
• Observe your yard and choose a location frequented by squirrels, birds, or other animals. You may choose a surface close to the ground or a raised surface such as a planter or wide fence.
• Make sure the surface is level and that both ears are equally accessible to animals.
• To prevent animals from knocking the feeding stand over, secure the feeding stand with a small nail (be careful not to split the wood), duct tape, string, clamp, or other method.

• To start the experiment, take a picture and note the time. Get down to the level of the feeding stand when you take the photo. Make sure that both ears are in focus and that you can read the barcode numbers.
• Enter your observation at GMOexperiment.com .
• About 24 hours later, take a second photo and note the time. Try to get in the same position as you were for the starting photo. If you notice all the corn is gone in less than 24 hours, you can end the experiment early.
• Once you finish making observations and enter them at GMOexperiment.com , close out the experiment.
• As best you can, enter the weather for your 24 hour experimental period. Note if there were any occurrences such as high winds or very cold temperatures that could prevent animals from foraging.
• Select the type of precipitation (none, rain, snow, etc.).
• Optional. Enter high and low temperatures in Celsius for your location.
• Optional. Enter the total precipitation in millimeters for your location. To find precipitation data, go to the National Weather Service and enter your location, then switch to metric (note the NWS site does not work well in Chrome).
• Optional. Warning. Adults only. Wear gloves to avoid injury. If you measured the starting weights, use the same scale to measure the ending weights of each ear and record the data in grams . If any kernels fall off when you remove the ears from the stand, make sure you include the kernels with their ear when you weigh it.

Additional Data Collection

• Optional. If both ears of corn have kernels remaining and if the corn did not get wet, you may retry the experiment with the same ears. Start the experiment from the beginning with a new start photo. You may move the feeding stand to a new location before retrying the experiment.
• Optional. You may take additional photos or other types of data during the 24 hour experiment and add them at GMOexperiment.com . Be careful and avoid moving the feeding stand. Minimize your time near the feeding stand so you don’t scare away the animals.

Additional data suggestions: Take photos at regular time intervals (1 hour, 2 hours) during the experiment to see how quickly the ears are eaten. These can be uploaded to the website like the start and end photos. You can use your observations from the first experiment to plan how far apart to take photos for the second experiment.

The GMO Corn Experiment is a project of Biology Fortified, Inc. We are an independent non-profit organization dedicated to encouraging science-based discussions in food and agriculture.

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The GMO Experiment was made possible by donations from kind people just like you. Please consider supporting us through donations, volunteering, and more .

• Published on Nov 18, 2015
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