Remote fruit fly detection using computer vision and machine learning-based electronic trap - Molina-Rotger, Miguel; Morán, Alejandro; Miranda, Miguel Angel; Alorda-Ladaria, Bartomeu (Frontiers in Plant Science, 2023)
Intelligent monitoring systems must be put in place in order to practice precision agriculture.With the use of these technologies, real-time data collection and analysis can be automated, enabling farmers to more efficiently monitor and optimize their operations. As a result, specific information on the soil's characteristics, such as humidity, crop quality, and the presence of pests or diseases, can be collected. In this context, computer vision and artificial intelligence techniques can be applied to monitor and prevent pests, such as that of the olive fly. These techniques are a tool to discover patterns and abnormalities in the data, which helps the early detection of pests and the prompt administration of corrective measures. However, there are significant challenges due to the lack of data to apply state of the art Deep Learning techniques. In this article, the detection and classification of the olive fly using the Random Forest and Support Vector Machine algorithms, as well as their application in a electronic trap version based on a Raspberry Pi B+ board, will be examined. The combination of the two methods is suggested to increase the accuracy of the classification results while working with a small training data set. Combining both techniques for olive fly detection yields an accuracy of 89.1%, which increases to 94.5% for SVM and 91.9% for RF when comparing all fly species to other insects.