Design of novel intelligent electronic trap for early detection and monitoring of tomato crops pest Tuta Absoluta using Deep learning

Abstract

To control insect pests and reduce the destruction of agricultural crops, the process of detection and monitoring of pests is an urgent need at present time. Due to the tremendous development in technology, the traditional methods used in laboratories considered a waste of time and human efforts. In this research, a new data set collected and published for the first time, novel electronic trap designed with intelligent system to detect pests in tomato crop, and monitor the spread of the pest periodically and continuously based on the collected dataset. As the designed intelligent system firstly consists of a novel trap designed in a way that contains six colored sticky traps to catch insects continuously, controlled by L293D IC to rotate the motor, a digital camera used to provide the system with real images at periodic intervals. To detect pest, the (YOLOv11, YOLOv9, YOLOv8 and YOLOv5) used for this purpose. The Tuta Absoluta pest used for the detection and monitoring process of the designed novel intelligent system. The results used in the system; the precision was 95.9%, recall was 92.5%, Mean Average Precision (mAP 0.5) was 94% and F1 score was 94% and the results were promising. As compared to other models of (YOLOv11, YOLOv9, YOLOv8 and YOLOv5), the YOLOv5x shows that its higher results than other models. This system is easy to use and accurate in providing the information required monitoring the spread of the insect pest, therefore it could use in modern agricultural applications.