A Deep Learning-based System for Automated Plant Disease Severity Detection and Fertilizer Optimization

Abstract

Abstract: Because food is a basic requirement for all living beings on Earth, agriculture is especially important in our daily life. Agriculture is our main source of food. In addition to plant diseases that impede the growth and quality of food crops, agriculture works to generate food to feed the growing population. We'll look at five plants: bitter gourd, mango tree, spinach, tomato, and hibiscus. This study proposes a CNN-based technique for early plant disease diagnosis. The approach consisted of three steps: image segmentation, feature extraction, and picture pre-processing. The results of these three processes are combined to form a Convolutional Neural Network (CNN) classifier. To research and analyse a plant, an input image of the damaged portions is obtained and compared to the desired dataset. The disease is then anticipated, along with therapeutic treatments. Once the disease has been recognized, the quantity of pesticides, recommended application place, and chemicals themselves will be displayed. It will also identify the nearest location where pesticides are available.

Plant phenotyping relevance

植物病害の症状画像から病害状態・重症度を推定するCNNベースの画像解析法が研究の中心であり、単なる生物学的実験のルーチン測定ではない。

Code and data availability

The paper describes a CNN-based plant disease detection system using tomato leaf images from Kaggle, but provides no public URL, deposit, or availability statement for any dataset, code, or trained model. The Kaggle mention is generic with no actionable link, and no author code or model checkpoints are described as公开ly

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