MACHINE LEARNING IN AGRICULTURE FOR CROP DISEASES IDENTIFICATION: A SURVEY

Abstract

The field of computer science known as machine learning is used to create algorithms that have the ability to self-learn or learn on their own. This is how the phrase "Machine Learning" came to be. Artificial intelligence in-cludes a subfield called machine learning. These days, machine learning and deep learning techniques are frequently used to classify and recognize leaf diseases. Recognizing leaf disease at an early stage is crucial in agricultural fields for all crops. Accurate disease detection at an early stage helps farmers boost production and their economy. The suggested study is a survey of more than 40 research papers that classify and identify plant leaf diseases using various machine learning and deep learning algorithms. It also discuss-es machine learning, its application to agriculture, as well as its benefits and drawbacks. Develop an automatic disease detection system for leaf disease classification and detection using web-based or mobile-based applications for future work. Using this survey to build a more accurate model for leaf disease classification and detection using machine learning with a wide range of datasets. This will be very beneficial for farmers to boost productivity and build their economies.

Plant phenotyping relevance

植物葉の病害を画像から分類・識別する機械学習手法のサーベイであり、植物の病害状態を推定するフェノタイピング手法のレビューが中心です。

Code and data availability

This is a survey article on machine learning for crop disease identification. It presents no authors' phenotype datasets, plant images, analysis code, or trained models of its own; all cited datasets and works belong to prior studies, and the article only recommends that researchers make datasets public. No paper-qual,

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