Tomato Pest Detection Using Convolutional Neural Network in Bangladesh

Abstract

One of the major commercial crops, tomatoes provide a lot of vitamins and can also be consumed as fruit. Throughout its lifecycle, the tomato is affected by a number of diseases and pests. Lack of prompt management might result in decreased yields or possibly crop loss. The most significant stage in finding out how to effectively control diseases and pests and support vegetable farmers in enhancing tomato production is to accurately identify the diseases and insect pests. The analysis and classification of plant diseases are currently the focus of a wide range of research studies based on image processing. These technologies are useful for rapidly detecting pests and illnesses in plants. Plant pests are still a significant issue for the agricultural sector. Aphids, whiteflies, thrips, red spider mites, and looper caterpillars are just a few of the pests that harm tomato plants. Faster detection of these pests on tomato plants might lead to early treatment and dramatically reduced financial losses. Five different types of tomato bugs were investigated. In this study, we compared two approaches to locating typical tomato bugs. In the first method, CNN is used, whereas, in the second method, CNN is combined with a random forest classifier. The contrast has been provided. We have also used some classifiers to measure the accuracy. This study will serve as a guide for the engineering application of intelligent disease and pest detection.