Contemporary and Futuristic Intelligent Technologies for Rice Leaf Disease Detection
Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal
Abstract
Rice also called Oryza sativa is the world's third most widely consumed granule, with about 50% of the people in the world consuming it as a fasten food. Rice cultivation was favored over other crops by some farmers due to its broad adaptability in varied habitats and lower cultivation risk as the world's population is increasing gradually, and estimated that by 2050, 14,886 million tons of food would be needed to fulfill the demand. According to several surveys, about 70% of India's population is dependent on agriculture, either directly or indirectly, and agribusiness provides around 17% of the country's overall GDP. There is a variety of factors that cause various diseases and infections in the plants like soil conditions, environmental conditions, selection of undesirable crops, pest weeds, low-quality manure, and various plants Disease. Agricultural productions are majorly affected by various Plant diseases. The overall crop production of the country is directly impacted by these factors. To prevent the disease infection continuous monitoring of the plants is required. So, the main concern in agriculture is the early identification of plant diseases. This paper discusses the various Machine Learning (ML)/Deep Learning (DL) and Internet of Things (IoT) techniques that are used for the early identification of rice leaf diseases with their performance measure further also discuss the various achievements, challenges, and future suggestions for the early diagnosis of rice plant diseases.