Design and Development of a Plant Leaf Disease Identification System using Improved Deep Learning Strategy

Abstract

The agriculture sector is the most important contributor to expanding economies and people because of the vital role it plays in providing high-quality food. It is possible for plant diseases to cause significant decreases in food production as well as the extinction of endangered species. Improving food production quality and minimizing economic losses can be achieved by early identification of plant diseases utilizing reliable or automated detection techniques. Deep learning has recently made great strides in improving the accuracy of object detection and picture categorization systems. The authors of this research proposed a new method for detecting plant diseases; they called it the Learning Network for Disease Identification (LNDI), and they cross-validated it with the Convolutional Neural Network (CNN). Differentiating between diseases in different plant species is a key goal of this research. Several plant kinds, including as fruits and vegetables, wheat, raisins, sugarcane, and lettuce have been incorporated into the system's development process. A wide variety of herbal ailments can also be diagnosed by the computer. Using a large dataset consisting of photos of diseased and healthy plant leaves, the specialists trained deep learning models to detect and distinguish between various plant illnesses and those that went unnoticed. Biological research and agricultural institutes are only two of the many potential uses for plant leaf disease detection. Research into plant leaf disease detection is necessary because it has the potential to improve crop monitoring by automatically identifying disease signs on plant leaves as they emerge.

Plant phenotyping relevance

植物葉の病徴を画像から識別する深層学習手法を開発し、CNNとの交差検証を行っており、病害状態の表現型取得が研究の中心です。

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