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Disease Detection in Plants using CNN

Mamta Gahlan

Abstract


In this paper, we proposed a convolutional neural network based model to conduct plant disease detection and diagnosis utilizing simple leaf images from both healthy and diseased plants. For model training, an accessible dataset comprising around 87,000 photos representing 15 diverse plant species within 38 distinct classes of [plant, disease] combinations, including healthy plants, was utilized. The performance of several model architectures was trained to discover the optimum [plant, disease] pairings (or healthy plants). This model shows extremely high success rate and makes it a very valuable tool for advising as an early warning. It also allows for the development of an integrated system for plant disease diagnosis that is effective in actual growing environments. The performance of the proposed model is compared with existing state of the art models and proved to perform well.

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References


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