Role of Artificial Intelligence in Pest Management

Agricultural pests cause 20-40 per cent loss of global crop production every year as reported by the Food and Agriculture Organization [1,2]. Excessive usage of pesticides to manage pests leads to severe problems. Smart agriculture presents the best option for farmers to apply artificial intelligence (AI) techniques integrated with modern information and communication technology to manage these harmful insect pests. Artificial intelligence (AI) is a broad term encompassing Machine Learning (ML), deep learning, computer vision etc. The core part of AI is Machine Learning (ML). Applications of AI in agricultural entomology are helpful in taxonomic studies, ecological studies and pest management. In this chapter, main focus is on AI usage in pest management through pest detection, monitoring, prediction and identification thereby helping in timely pest management. Several applications such as Plantix, Leaf-Byte, Bioleaf, Cotton Ace, Apizoom etc have been developed to diagnose and identify insect pests to manage them. Some of the important usage of AI in pest management discussed in the chapter are as follows: Chen et al. [3] developed an AIoT Based Smart Agricultural System for Tessaratoma papillosa (lychee giant stink bug) detection with 90 per cent accuracy. Karar et al. [4] developed a mobile application for the detection of five groups of insect pests viz., aphids, leaf hoppers, flax budworm, flea beetles, and red spider mites with 99.0 per cent accuracy for all tested pest images. As monitoring the insect pests is a crucial component in pheromone-based pest management systems, Ding and Taylor [5] developed an automatic moth detection method using AI with images collected from pheromone traps for timely pest management unlike the conventional counting methods. Liu et al. [6] using artificial intelligence developed an autonomous robotic vehicle in natural farm scene for the recognition of pyralidae insects with 94.3 per cent recognition accuracy for effective management of pyralidae insects in the farm. Potamitis and Rigakis [7] developed a smart trap for automatic remote monitoring of Rhynchophorus ferrugineus (Red palm weevil) to take necessary steps for controlling it based on ETLs. Selvaraj et al. [8] developed a model based on AI for banana diseases and pests detection with significant high success rate which is useful for early disease and pest detection. Hence, integrating artificial intelligence with Entomology will help in effective & timely management and forecasting of pests and diseases.