Systematic Review on Maize Plant Disease Identification Based on Machine Learning

Abstract

Agriculture plays a crucial role in everyone's life. In this technological world, 75 out of 100 are taking steps towards automated workflow solutions rather than staying in the same position of manual solution replica of analyzing the product to detect the disease affecting the product's production. This study focuses primarily on wheat, which is a significant crop farmed globally owing to its substantial contribution to human nutrition and provides for around 14% of global food consumption. However, various diseases affect wheat yield, which can reduce 30% (31 million metric tons approx.) of wheat production, out of which 106.41 million measured tones of wheat for 2021-22 in India, a severe hazard to food security. Therefore, it is required to early detection of the disease during the growing stage of the plant by applying plant disease detection approaches. While analyzing the product, we would use various techniques to classify the classes. To perform various operations to detect diseases, we collected different information and images related to wheat which we considered a dataset. The dataset would help us concentrate on the loopholes to work on so that the algorithm would have a more accurate percentage to isolate the disease in plants, especially wheat.