A Deep-Learning-Based Detection Approach for the Identification of Insect Species of Economic Importance

SIMPLE SUMMARY: This study aims at developing a machine-learning-based classification approach to recognize insect species of economic importance. Two tephritid pest species with similar shape and locomotory patterns (e.g., the Mediterranean fruit fly Ceratitis capitata, and the olive fruit fly Bactrocera oleae) were used as model organisms. The proposed method, based on a convolutional neural network (CNN), accurately detects and discriminates moving C. capitata and B. oleae adult individuals in real-time. These results importantly contribute to the development of autonomous pest monitoring methods, to intervene with tailored measures instantaneously and remotely. Overall, this study promotes sustainable and efficient crop protection approaches based on integrated pest management and precision techniques. ABSTRACT: Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. Traditional monitoring is labor intensive, time demanding, and expensive, while machine learning paradigms may support cost-effective crop protection decisions. However, previous studies mainly relied on morphological images of stationary or immobilized animals. Other features related to living animals behaving in the environment (e.g., walking trajectories, different postures, etc.) have been overlooked so far. In this study, we developed a detection method based on convolutional neural network (CNN) that can accurately classify in real-time two tephritid species (Ceratitis capitata and Bactrocera oleae) free to move and change their posture. Results showed a successful automatic detection (i.e., precision rate about 93%) in real-time of C. capitata and B. oleae adults using a camera sensor at a fixed height. In addition, the similar shape and movement patterns of the two insects did not interfere with the network precision. The proposed method can be extended to other pest species, needing minimal data pre-processing and similar architecture.