Erratum to: The accuracy of farmer-generated data in an agricultural citizen science methodology

Steinke, Jonathan; Etten, Jacob van; Zelan, Pablo Mejía

doi:10.1007/s13593-017-0446-6

Cited by 1 publication

(1 citation statement)

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“…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 nonstandardized resolutions and uncontrolled background.…”

Section: Discussionmentioning

confidence: 99%

Instance Segmentation to Estimate Consumption of Corn Ears by Wild Animals for GMO Preference Tests

Adke

Mogel²,

et al. 2021

Front. Artif. Intell.

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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 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 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.

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Section: Discussionmentioning

confidence: 99%