Abstract
Plant disease detection, one of the most considerable and primary threats in precision agriculture, aims to find the diseased instances from plant leaf images of specified categories. Though researchers have made several attempts in recent years, there is room for research to develop models to detect and segment plant diseases at different growth stages in agriculture fields. In this study, practical multi-task automated plant leaf disease detection and segmentation frameworks are developed based on EfficientDet and Mask_RCNN deep learning models to address this problem. A total of 9,304 images, annotated manually from two publicly available datasets, are considered for training the two proposed models. Compared with the benchmark state-of-art models, the proposed plant disease detection and segmentation models achieve a mean average precision (mAP) of 75.16% and 76.94%, respectively. From empirical observations, we anticipate that proposed frameworks will boost plant disease detection, and more generally, accelerate the development of an automated and effective plant disease detection system.
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Kavitha Lakshmi, R., Savarimuthu, N. (2022). A Deep Learning Paradigm for Detection and Segmentation of Plant Leaves Diseases. In: Uddin, M.S., Bansal, J.C. (eds) Computer Vision and Machine Learning in Agriculture, Volume 2. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9991-7_14
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DOI: https://doi.org/10.1007/978-981-16-9991-7_14
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