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.

该研究通过使用深度学习技术，分析无人机拍摄的图片，自动识别玉米植株中的异常区域并分类成低、中、高或无异常。研究旨在能在最早的阶段识别异常，以最大化潜在治疗的有效性。同时，该系统为人工标注员提供宝贵的信息，帮助他们只关注一小部分图片进行真实数据收集。