Peaches Detection Using a Deep Learning Technique - A Contribution to Yield Estimation, Resources Management, and Circular Economy

Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.
The authors are thankful to Fundação para a Ciência e Tecnologia (FCT) and R&D Unit “Center for Mechanical and Aerospace Science and Technologies” (C-MAST), under project UIDB/00151/2020, for the opportunity and the financial support to carry on this project. The contributions of Hugo Proença and Pedro Inácio in this work were supported by FCT/MEC through FEDER—PT2020 Partnership Agreement under Project UIDB//50008/2021.