Instance Segmentation to Estimate Consumption of Corn Ears by Wild Animals for GMO Preference Tests - Adke, Shrinidhi; Haro von Mogel, Karl; Jiang, Yu; Li, Changying (Frontiers in Artificial Intelligence, 2021)
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 ear from these images, which will aid in testing the hypothesis in the GMO Corn Experiment. This approach uses Mask Regional Convolutional Neural Network (Mask R-CNN) for the instance segmentation task. Based on image data annotation, two approaches for segmentation were discussed: identifying whole corn ears and bare ear 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. The 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 labelled test data with R2 = 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 that is the focus of the GMO Corn Experiment. These approaches may also be applied to new hypotheses relevant to crop yield, biotic and abiotic stresses, and phenotypes that are difficult to examine through traditional methods.