Machine Learning Techniques for Plant Disease Detection
Divyanshu Varshney, Burhanuddin Babukhanwala, Javed Khan, Deepika Saxena, Ashutosh Kumar Singh
Abstract
Undoubtedly, agriculture is an essential source of livelihood, which stands as a backbone of Indian economy. The plant production is severely affected due to various kinds of diseases, which if accurately and timely detected, could raise health standards and economic growth significantly. The traditional approachesof disease detection and classification involves an immense amount of time, an intense amount of labor and constant monitoring of the farm. By using disease detection methods, diseases caused by bacteria, viruses and fungi are often avoided. Within the upkeep of agricultural goods, crop protection plays a critical role. Techniques of Machine Learning are often used to identify the affected leaf images. The various machine learning algorithms used to determine whether a plant is infected or not with a disease, are discussed in this study. It was done in various steps, such as image acquisition, feature extraction, categorization of the illness and result display. This paper also needs to carry out an accurate study of various techniques for the identification of diseases of plants. The aim is to identify the plant diseases using image analysis. It also, after detection of the illness, says the name of fertilizer to be used. The pests and insects accountable for the pandemic are also described.