A comprehensive literature review on the detection of tomato leaf disease by deep learning and machine learning models

Le résumé fourni par la source

In so far as everyone needs food, farming helps to provide for this basic requirement. Despite making up only 15.4% of the country's GDP, the agriculture sector is crucial to India since it creates a large number of job possibilities. In addition to providing useful information, the use of machine learning is improving farming by preventing or minimizing losses. Machine learning includes situations in which a machine may learn and make choices based on predetermined criteria. It may be classed as an area of artificial intelligence (AI). Artificial Neyral Networks (ANN), one of hottest artificial intelligence approaches of the present day, are extended by deep learning, a popular and contemporary data analysis tool in the last ten years. As the world's population rises, agriculture becomes more and more significant.This article discusses modern machine learning applications in agriculture and how they address issues before during, and after agricultural production.Numerous deep learning and machine learning methods, databases performances were reviewed in the publications. They grouped the approaches they looked at according to the most popular techniques, the kinds of data used, the performance metrics, and the results of these analyses.To detect tomato leaf diseases, machine learning techniques and image processing are used, albe it their accuracy is lower. Initially, tomato leaf diseases are automatically identified utilizing traditional image processing and machine learning techniques, yet the accuracy is lower.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.