Early identification of abnormalities in plants is an important task for ensuring proper growth and achieving high yields from crops. Precision agriculture can significantly benefit from modern computer vision tools to make farming strategies addressing these issues efficient and effective. As farming lands are typically quite large, farmers have to manually check vast areas to determine the status of the plants and apply proper treatments. In this work, we consider the problem of automatically identifying abnormal regions in maize plants from images captured by a UAV. Using deep learning techniques, we have developed a methodology which can detect different levels of abnormality (i.e., low, medium, high or no abnormality) in maize plants independently of their growth stage. The primary goal is to identify anomalies at the earliest possible stage in order to maximize the effectiveness of potential treatments. At the same time, the proposed system can provide valuable information to human annotators for ground truth data collection by helping them to focus their attention on a much smaller set of images only. We have experimented with two different but complimentary approaches, the first considering abnormality detection as a classification problem and the second considering it as a regression problem. Both approaches can be generalized to different types of abnormalities and do not make any assumption about the abnormality occurring at an early plant growth stage which might be easier to detect due to the plants being smaller and easier to separate. As a case study, we have considered a publicly available data set which exhibits mostly Nitrogen deficiency in maize plants of various growth stages. We are reporting promising preliminary results with an 88.89\% detection accuracy of low abnormality and 100\% detection accuracy of no abnormality.

翻译：植物异常早期识别是保障作物健康生长与高产的重要任务。精准农业可借助现代计算机视觉工具显著提升种植策略的有效性与效率。由于农田面积通常较大，农民需人工巡检大片区域以判断植株状态并采取相应措施。本研究探讨了基于无人机图像自动识别玉米植株异常区域的问题。通过深度学习技术，我们开发了一种能够独立于植株生长阶段检测不同异常程度（低、中、高或无异常）的方法。核心目标是在最早阶段识别异常，以最大化潜在处理措施的有效性。同时，该系统可为人工标注者提供有价值的信息，通过辅助其聚焦于更小规模的图像集，提高真实数据采集效率。我们尝试了两种互补方法：第一种将异常检测视为分类问题，第二种则视为回归问题。两种方法均可泛化至不同异常类型，且不预设异常发生于早期生长阶段（此时植株较小易分离，可能更易检测）。作为案例研究，我们使用了公开数据集，该数据集主要呈现不同生长阶段玉米植株的氮缺乏症状。初步结果令人振奋：低异常检测准确率达88.89%，无异常检测准确率达100%。