Chilli Plant Disease Detection and Classification using DenseNet CNN Approach

Abstract

To fulfil the food requirement and economic growth, farming plays a very important role. Thus Farmers are the most important people in the world. Be it the smallest or the largest country, Because of them only we are able to live on the planet. Precision agriculture is the new trending term in the field of technology whose main motive is to reduce the workload of the farmers and increase the productivity of the farms by using technologies. So the aim of this work is to detect the disease of the plant by classifying their leaves using deep learning algorithm. For this work chilli plants are considered, because of their economic importance. And there are various problems in chilli production due to the presence of various micro-organisms and pathogens and The plant disease detection can be done by observing the spot on the leaves of the affected plant. The method here adopting to detect plant diseases is image processing using Dense Net based Convolution neural network (CNN). CNN will be used for leaf image classification and will produce the good results with a good accuracy.

Plant phenotyping relevance

病斑を含む植物葉画像から病害を分類する画像解析手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法に該当します。

Code and data availability

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