Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches

• URL: http://arxiv.org/abs/2310.13201v1
• Date: Fri, 20 Oct 2023 00:06:42 GMT
• Title: Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches
• Authors: Aminul Huq, Dimitris Zermas, George Bebis
• Abstract summary: Early identification of abnormalities in plants is an important task for ensuring proper growth and achieving high yields from crops. We have developed a methodology which can detect different levels of abnormality in maize plants independently of their growth stage. Preliminary results show an 88.89% detection accuracy of low abnormality and 100% detection accuracy of no abnormality.
• Score: 0.6226366855893847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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