Biotic Stress Management in Field Crops Using Artificial Intelligence Technologies

IndiaIndia is an agriculture dependent country and due to changing climatic scenarios, nation is facing challenges in ensuring food securitySecurity to its ever-increasing population. Abiotic and biotic stresses have a big impact on crop productivityProductivity. Due to climate change, biotic stresses, such as insects and diseases in various field crops are responding differently. The Food and Agriculture Organisation of the United Nations (FAO) estimates that crop insects and diseases are responsible for 20–40% of the annual losses in global food productionProduction. Inexperienced pesticide users run the risk of causing infections and insects to become resistant, which greatly reduces the host plant's capacity for self-defence. Various scientists are studying the intricate relationship between plants, pests, pathogens, and environment using a range of computer modelling and simulation tools, including artificial neural networks and conventional multiple regression methods, which can be used to forecast the occurrence of insects and diseases. Artificial neural network (ANN) techniques have received a lot of interest recently, partly due to their broad range of applicability and ease of use for treating complex issues even with noisy and erroneous data. The models enable research into the factors influencing pest outbreaks and the development of yield loss mitigation techniques. By utilising need-based chemicals, they also guarantee the safety of the operator, the consumer, and the environment. By focusing on the problems encountered with the methodology, procedures, and data employed, the main aim of this chapter is to describe in depth the artificial intelligence and machine learning algorithms used to anticipate plant insects and diseases.