Assessing the efficacy of machine learning techniques to characterize soybean defoliation from unmanned aerial vehicles

Severe crop defoliation caused by insects and pests is linked to low agricultural productivity. If the root cause is not addressed, severe defoliation spreads, damaging whole crop fields. Understanding which areas are afflicted by severe defoliation can help farmers manage crops. Unmanned Aerial Vehicles (UAV) can fly over whole crop fields capturing detailed images. However, it is hard to characterize crop defoliation from aerial images that include multiple, overlapping plants with confounding effects from shadows and lighting. This paper assesses the efficacy of machine learning techniques to characterize defoliation. Given an UAV image as input, these techniques detect if severe defoliation is present. We created a labeled data set on soybean defoliation that comprises over 97,000 UAV images. We compared machine learning techniques ranging from Naive Bayes to neural networks and assessed their efficacy for (1) correctly characterizing images that contain defoliated crops and (2) avoiding wrong characterizations of healthy crops as defoliated. None of the techniques studied achieved high efficacy on both questions. However, we created DefoNet, a convolutional neural network designed for detecting crop defoliation that produces models that can be efficacious for either question. If adopted in practice, DefoNet models can guide decision making for mitigating crop yield losses due to defoliating insects.

昆虫和害虫导致的严重作物落叶与农业生产率低下有关。如果不解决根本原因，严重的落叶现象会蔓延，损害整片农田。了解哪些区域受到严重落叶的影响有助于农民管理作物。无人机（UAV）可以飞越整片农田，拍摄详细的图像。然而，从包含多个相互重叠的植物且受阴影和光照干扰影响的航拍图像中很难描述作物落叶的特征。本文评估了机器学习技术用于描述落叶特征的效果。以无人机图像作为输入，这些技术检测是否存在严重落叶。我们创建了一个关于大豆落叶的标注数据集，其中包含超过97000张无人机图像。我们比较了从朴素贝叶斯到神经网络等机器学习技术，并评估了它们在以下两方面的效果：（1）正确描述包含落叶作物的图像；（2）避免将健康作物错误地描述为落叶作物。所研究的技术在这两个问题上都没有取得高效能。然而，我们创建了DefoNet，这是一种专为检测作物落叶而设计的卷积神经网络，它所生成的模型对上述两个问题中的任何一个都可能有效。如果在实践中采用，DefoNet模型可以指导决策，以减轻因食叶昆虫导致的作物产量损失。