Automatic pest detection on bean and potato crops by applying neural classifiers

Abstract

Instrumentation and Artificial Intelligence (AI) recognition techniques were developed for automatic pest detection. This system is based on pest detection and monitoring, and it improves the efficiency of vegetable and fruit farming and food production. This paper presents an automatic pest detection system that applies artificial neural networks. The system automatically detects two defoliating pests on potato and bean crops: Mexican Bean Beetle (MBB) and Colorado Potato Beetle (CPB) in the adult stage. The neural classifiers utilized for the beetle detection are RSC (Random Subspace Classifier) and LIRA (Limited Receptive Area). The MBB images that were employed as inputs to the classifiers were obtained on Mexican plantations. The CPB images were collected from various Internet sources. We compared the results obtained with both classifiers on image databases. The RSC classifier demonstrates the better result for recognition, which is 89%, while LIRA presents a recognition rate of 88%. These results are good for pest detection and can be used for the diagnosis of pest locations in crops. The purpose was to contribute to the development of automatic detection applications based on images of potato and bean plantations. In Mexico and other countries, it is of great importance to solve pest problems in agriculture. We chose insect recognition due to the importance of potato and bean crop production and consumption. Pest detection in the adult phase is of high priority because of the high rate of crop defoliation and destruction. Our automatic pest-detection system can be employed in pest recognition in monitoring activities.