Accurate identification of tomato ripeness and precise detection of picking points is the key to realizing automated picking. Aiming at the problems faced in practical applications, such as low accuracy of tomato ripeness and picking points detection in complex greenhouse environments, which leads to wrong picking, missed picking, and fruit damage by robots, this study proposes the YOLO-TMPPD (Tomato Maturity and Picking Point Detection) model. YOLO-TMPPD is structurally improved and algorithmically optimized based on the YOLOv8 baseline architecture. Firstly, the Depthwise Convolution (DWConv) module is utilized to substitute the C2f module within the backbone network. This substitution not only cuts down the model’s computational load but also simultaneously enhances the detection precision. Secondly, the Content-Aware ReAssembly of FEatures (CARAFE) operator is utilized to enhance the up-sampling operation, enabling precise content-aware processing of tomatoes and picking keypoints to improve accuracy and recall. Finally, the Convolutional Attention Mechanism (CBAM) module is incorporated to enhance the model’s ability to detect tomato-picking key regions in a large field of view in both channel and spatial dimensions. Ablation experiments were conducted to validate the effectiveness of each proposed module (DWConv, CARAFE, CBAM), and the architecture was compared with YOLOv3, v5, v6, v8, v9, and v10. The experimental results reveal that, when juxtaposed with the original network model, the YOLO-TMPPD model brings about remarkable improvements. Specifically, it improves the object detection F1 score by 4.48% and enhances the keypoint detection accuracy by 4.43%. Furthermore, the model’s size is reduced by 8.6%. This study holds substantial theoretical and practical value. In the complex environment of a greenhouse, it contributes significantly to computer-vision-enabled detection of tomato ripening. It can also help robots accurately locate picking points and estimate posture, which is crucial for efficient and precise tomato-picking operations without damage.

Pest infestations have always been a major factor affecting tea production. Real-time detection of tea pests using machine vision is a mainstream method in modern agricultural pest control. Currently, there is a notable absence of machine vision devices capable of real-time monitoring for small-sized tea pests in the market, and the scarcity of open-source datasets available for tea pest detection remains a critical limitation. This manuscript proposes a YOLOv8-FasterTea pest detection algorithm based on cross-domain transfer learning, which was successfully deployed in a novel tea pest monitoring device. The proposed method leverages transfer learning from the natural language character domain to the tea pest detection domain, termed cross-domain transfer learning, which is based on the complex and small characteristics shared by natural language characters and tea pests. With sufficient samples in the language character domain, transfer learning can effectively enhance the tiny and complex feature extraction capabilities of deep networks in the pest domain and mitigate the few-shot learning problem in tea pest detection. The information and texture features of small tea pests are more likely to be lost with the layers of a neural network becoming deep. Therefore, the proposed method, YOLOv8-FasterTea, removes the P5 layer and adds a P2 small target detection layer based on the YOLOv8 model. Additionally, the original C2f module is replaced with lighter convolutional modules to reduce the loss of information about small target pests. Finally, this manuscript successfully applies the algorithm to outdoor pest monitoring equipment. Experimental results demonstrate that, on a small sample yellow board pest dataset, the mAP@.5 value of the model increased by approximately 6%, on average, after transfer learning. The YOLOv8-FasterTea model improved the mAP@.5 value by 3.7%, while the model size was reduced by 46.6%.

Pomegranate is a highly valued fruit known for its delicious seeds packed with health benefits and also a popular ornamental tree, as well as traditional medicine, but traditional management of the orchard heavily relies on manual laborious processes, leading to low efficiency and increased cost. Using machine vision to monitor its fruit development in real time can achieve accurate intelligent management and save a lot of lab our costs. Faster and lighter weight detection model is crucial to machine vision. The proposed YOLO-Punica model is built on an improved version of You Only Look Once version 8n (YOLOv8n) algorithm, resulting in a lightweight and faster detection model specifically designed for monitoring pomegranate fruit development in real time. The optimization includes the integration of two innovative modules, the Dual Path Downsampling Module (DPDM) and the Cross-Scale Feature Fusion Module (CCFM). The incorporation of the DPDM into the backbone network significantly enhances detection precision and computational efficiency. Additionally, the integration of both CCFM and DPDM into the neck structure, substantially reduces parameters, memory consumption, and overall model size, while improving operational efficiency and detection accuracy. The implementation of DPDM and CCFM in the YOLOv8n framework results in a lighter model, faster processing speeds, and improved detection accuracy. Comparative test results indicate that YOLO-Punica achieves reductions of 45.8% in parameters, 28% in Giga Floating-point Operations Per Second (GFLOP), and 43.7% in model size relative to YOLOv8n, while realizing a mean Average Precision (mAP) of 92.6%, surpassing YOLOv8n by 0.98%. Furthermore, the model is capable of processing images at a rate of 14.3 frames per second on embedded devices demonstrating its applicability for real-time detection of pomegranate fruit development, even in low computational power environments. This research not only provides technical support for intelligent detection of pomegranate fruit development, but also provide a new perspective for enhancing machine vision models in other agricultural contexts.