Smart Pesticide Recommendation System Using Deep Learning Techniques

The world’s population relies heavily on the agriculture sector to meet their daily needs. One of the main problems that farmers and crops encounter is pests. Pests can directly injure plants and stunt their growth by growing in unfavorable environments. Pests are responsible for the loss of 20 to 40% of the world’s agricultural production, which has a serious detrimental effect on the world economy. The greatest way for farmers to use knowledge and modern technology to get rid of these dangerous insect pests is through smart agriculture. This article suggests a web application that utilizes deep learning to automatically categorize pests in order to help farmers. Convolutional neural networks (CNN) are used by the program to recognize pests. To help farmers, a database of advised pesticides is connected with a list of agricultural pests. The nine pest groups known as aphids, armyworms, beetles, bollworms, earthworms, grasshoppers, mites, mosquitoes, sawflies, and stem borer have successfully validated this study. The work implements the prior review paper (Harsha et al in Comparative analysis of smart pesticide recommendation system using ML/AI, IEEE, [1]).