Tree fruit load calculation with image processing techniques

Abstract

Turkey holds a significant position in global olive production, with olives being a crucial component of its agricultural industry. The fruit load on trees directly correlates with olive tree yield, which in turn determines productivity. The Tabit Smart Agriculture R&D Center, located in the Koçarlı district of Aydın within Turkey’s Aegean region, conducted a study using the YOLOv3 Convolutional Neural Network model to estimate olive tree loads. The primary aim of this research was to offer a more precise and objective perspective on olive harvesting, moving away from subjective assumptions based on predictions. Olive trees, playing a significant role in Turkey’s agricultural output, are cultivated across various regions in the country. However, olive sales in Turkey still rely on approximations. To tackle this, image processing techniques were employed to introduce a more technological and practical approach to agricultural applications, particularly in estimating olive tree loads. Throughout the study, real-time datasets were generated by capturing images of olive trees at the Tabit Smart Agriculture R&D Center in Aydın. The focus was on accurately detecting and counting olives. After 6000 iterations, the obtained results were as follows: mAP 61%, Precision 70%, Recall 45%. The results of the study proved that the agricultural industry can actively shape the trajectory of future farming practices by adeptly embracing image processing techniques and deep learning models.