A Method of Modern Standardized Apple Orchard Flowering Monitoring Based on S-YOLO

Xinzhu Zhou, Guoxiang Sun (), Naimin Xu, Xiaolei Zhang, Jiaqi Cai, Yunpeng Yuan and Yinfeng Huang
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Xinzhu Zhou: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Guoxiang Sun: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Naimin Xu: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xiaolei Zhang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jiaqi Cai: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Yunpeng Yuan: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Yinfeng Huang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2023, vol. 13, issue 2, 1-17

Abstract: Monitoring fruit tree flowering information in the open world is more crucial than in the research-oriented environment for managing agricultural production to increase yield and quality. This work presents a transformer-based flowering period monitoring approach in an open world in order to better monitor the whole blooming time of modern standardized orchards utilizing IoT technologies. This study takes images of flowering apple trees captured at a distance in the open world as the research object, extends the dataset by introducing the Slicing Aided Hyper Inference (SAHI) algorithm, and establishes an S-YOLO apple flower detection model by substituting the YOLOX backbone network with Swin Transformer-tiny. The experimental results show that S-YOLO outperformed YOLOX-s in the detection accuracy of the four blooming states by 7.94%, 8.05%, 3.49%, and 6.96%. It also outperformed YOLOX-s by 10.00%, 9.10%, 13.10%, and 7.20% for mAP ALL , mAP S , mAP M , and mAP L , respectively. By increasing the width and depth of the network model, the accuracy of the larger S-YOLO was 88.18%, 88.95%, 89.50%, and 91.95% for each flowering state and 39.00%, 32.10%, 50.60%, and 64.30% for each type of mAP , respectively. The results show that the transformer-based method of monitoring the apple flower growth stage utilized S-YOLO to achieve the apple flower count, percentage analysis, peak flowering time determination, and flowering intensity quantification. The method can be applied to remotely monitor flowering information and estimate flowering intensity in modern standard orchards based on IoT technology, which is important for developing fruit digital production management technology and equipment and guiding orchard production management.

Keywords: intelligent agriculture; IoT technology; apple flowering monitoring; open world; swin transformer; SAHI (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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