Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning

Abstract

Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV–GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565; The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.

A Lightweight Remote Sensing Small Target Image Detection Algorithm Based on Improved YOLOv8

“…This algorithm replaced the traditional convolutional layers in the YOLOv8 neck module with lightweight GSConv and utilized a Slim neck design paradigm for reconstruction, thus reducing computational costs while maintaining model accuracy. Wang et al [23] proposed a model for Unmanned Aerial Vehicle (UAV) strawberry recognition based on YOLOv8. The improved YOLOv8 model incorporates a Shuffle Attention Module and a VoV-GSCSP Module, thereby enhancing both recognition accuracy and detection speed.…”

Improved YOLOv8 and SAHI Model for the Collaborative Detection of Small Targets at the Micro Scale: A Case Study of Pest Detection in Tea

“…In recent years, deep learning has found widespread applications across various sectors, particularly in agriculture [6][7][8]. Target detection using deep learning has gained prominence in computer vision research and is extensively employed in crop harvesting [9,10], pest and disease detection [11][12][13], yield prediction [14][15][16], unmanned farm monitoring [17,18], and other areas. Through the development of intricate parallel models, deep learning technology has effectively addressed challenges such as limited data resources, information integration difficulties, and the low efficiency of knowledge utilization in agricultural settings.…”