Optimization strategies of fruit detection to overcome the challenge of unstructured background in field orchard environment: a review

• Published in: Precision agriculture Vol. 24; no. 4; pp. 1183 - 1219
• Main Authors: Tang, Yunchao, Qiu, Jiajun, Zhang, Yunqi, Wu, Dongxiao, Cao, Yuhong, Zhao, Kexin, Zhu, Lixue
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2023; Springer Nature B.V
• Subjects: Agriculture; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Computer Science; Design optimization; Fruits; Life Sciences; lighting; Object recognition; Occlusion; Optimization; Orchards; Physics; precision; Remote Sensing/Photogrammetry; Review; Soil Science & Conservation; Statistics for Engineering; Deep learning; Fruit detection; Computer vision; Intelligent agriculture
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-023-10009-9

The demand for intelligent agriculture is increasing due to the continuous impact of world food and environmental crises. Focusing on fruit detection, with the rapid development of object detection technology, it is now possible to achieve high efficiency and high accuracy in fruit detection systems. However, detecting fruit with high precision in unstructured orchard environments remains particularly challenging. Such environments, which are composed of varying illumination conditions and degrees of occlusion, can be mitigated by certain strategies. To our knowledge, this is the first time that optimization strategies used in fruit detection have been reviewed. This review aims to explore methods for improving fruit detection in complex environments. First, we describe the common types of complex backgrounds found in outdoor orchard environments. Subsequently, we divide the improvement measures into two categories: optimization before and after image sampling. Next, we compare the test results obtained before and after the application of these improved methods. Finally, we describe the future development trends of fruit detection optimization technology in complex backgrounds. We hope that this review will inspire researchers to design their optimization strategies and help explore lower-cost and more robust fruit detection systems.