Olive fruits identification using AI and Edge Computing

Abstract

Estimation of yield in olive production is very important in order to enhance the overall management and economic returns from olive orchards. Conventional methods of yield estimation are time consuming and most of the times they are prone to mistakes. This thesis presents a new method that applies image segmentation and artificial intelligence on the edge devices to enhance the estimation of the olive yield. This work suggests that with the assistance of adopting the U-Net architecture, it is conceivable to segment olives from photographs taken in-field, under natural light. The images were segmented manually to obtain a special dataset with binary masks that allow to clearly distinguishing olives from the rest of the background and, consequently, provide accurate predictions of olive yield. These images were used to train the U-Net model, and evaluated on dice score and the mean average precision (mAP). This thesis also presents the benefit of image segmentation over the object detection approach in managing the issues of natural orchard environment and the importance of data processing on the edge devices such as real-time data analysis and minimal data transfer. From the findings of this study, it was concluded that the proposed method has the potential of greatly improving the precision of the olive yield estimation while at the same time being a more efficient approach that could be used on a large scale in the management of the orchard. The trained algorithm was deployed on NVIDIA Jetson Nano and tested on field images. Positive results along with low latency makes this research a vital push in the government's already planned increase in local olive production. The research belongs to the agricultural technology domain, as it identifies how AI can be applied in practice in actual farms.