Identification of green leafhoppers (cicadellidae) in vineyards through an automatic image acquisition system from yellow sticky traps associated with deep-learning

This work presents an innovative approach to expedite the identification process of green leafhoppers by combining a deep-learning algorithm with an automatic camera system that captured high-resolution images from yellow sticky traps. Identifying and monitoring agricultural insects are crucial for implementing effective pest management strategies. Conventional insect identification and counting methods can be time-consuming and labor-intensive, urging the need for efficient and accurate automated solutions. The deep learning algorithm based on convolutional neural networks (CNNs) learn discriminators from a diverse set of green leafhopper images. The model’s architecture was optimized to handle variations in lighting conditions, angles, and orientations commonly found in field settings. To assess the algorithm’s efficacy, the test images were also evaluated by human curation and results accounted for in terms of false positives and false negatives. The results demonstrated the algorithm’s capability to accurately identify green leafhopper species, improving the speed of identification compared to conventional methods while maintaining a high level of precision (80%), and a harmonic mean of the precision and recall (F1) of 0.85. The combination of a deep learning algorithm and real-time data acquisition allows a fast decision-making by technicians and researchers, supporting the implementation of pest management strategies, and demonstrates the promising potential for specific and sustainable pest monitoring, contributing to the progress of precision farming practices.