Abstract
The food security of any country may be jeopardized due to improper management of agricultural insect pests. Accurate pest detection and efficient pest control strategies must be employed in time to grow healthy crops for achieving food security in a country and worldwide. Hence, developing efficient and robust techniques to detect agricultural insect pests using computer vision approaches is one of the essential steps for timely managing insect pests. This chapter presents a short survey of deep learning-based object detection techniques focusing on insect pest detection and associated insect pest image datasets. Subsequently, a transfer learning-based custom You Only Look Once (YOLOv5) model is developed using the publicly available dataset IP102 for detecting agricultural insect pests with the help of computer vision approaches. The hyperparameters of the proposed insect pest detector are optimized using the genetic algorithm-based hyperparameter evolution method. The performance metrics of the proposed insect pest detector are found to be promising.
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