Abstract
Plant disease diagnosis is critical in agriculture since diseases frequently restrict plant production receptivity. Physical strategies to identify plant diseases are mostly timely, objecting, and lengthy. As a consequence, agricultural automation with automated identification of plant diseases is widely preferred. Most of the implemented models could only identify diseases of a particular plant using high-resolution images, which is quite expensive from a farmer's position. Because of the variation in leaf colors, aspect ratios, and congested backgrounds, detecting plant disease by low-quality images is difficult. This paper explores an efficient plant disease identification model that combines multiple plant diagnoses for low-resolution images using deep convolutional neural networks (DCNNs). This system acquires a multilabel classification to classify both the plant type and the specific disorder simultaneously.
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Rahman, A., Al Foisal, M.H., Rahman, M.H., Miah, M.R., Mridha, M.F. (2022). Deep-CNN for Plant Disease Diagnosis Using Low Resolution Leaf Images. In: Chen, J.IZ., Wang, H., Du, KL., Suma, V. (eds) Machine Learning and Autonomous Systems. Smart Innovation, Systems and Technologies, vol 269. Springer, Singapore. https://doi.org/10.1007/978-981-16-7996-4_33
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DOI: https://doi.org/10.1007/978-981-16-7996-4_33
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