This study aims to help researchers quickly understand the latest research status of kiwifruit picking robots to expand their research ideas. The centralized picking of kiwifruit is confronted with challenges such as high labor intensity and labor shortage. A series of social issues including the decline in agricultural population and population aging have further increased the cost of its harvest. Therefore, intelligent picking robots replacing manual operations is an effective solution. This paper, through literature review and organization, analyzes and evaluates the performance characteristics of various current kiwifruit picking robots. It summarizes the key technologies of kiwifruit picking robots, from the aspects of robot vision systems, mechanical arms, and the end effector. At the same time, it conducts an in-depth analysis of the problems existing in automatic kiwifruit harvesting technology in modern agriculture. Finally, it is concluded that in the future, research should be carried out in aspects such as kiwifruit cluster recognition algorithms, picking efficiency, and damage cost and universality to enhance the operational performance and market promotion potential of kiwifruit picking robots. The significance of this review lies in addressing the imminent labor crisis in agricultural production and steering agriculture toward intelligent and precise transformation. Its contributions are reflected in greatly advancing robotic technology in complex agricultural settings, generating substantial technical achievements, injecting new vitality into related industries and academic fields, and ultimately delivering sustainable economic benefits and stable agricultural supply to society.

Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70–99.8%, the accuracy of external defect grading is 88–95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56–100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future.

In recent years, with changes in dietary structure, beef has become the third most consumed meat in China after pork and chicken, with its consumption increasing by approximately 50%. The quality and commercial value of beef vary considerably across different muscles. However, due to the high similarity in the appearance of beef cuts and strong background interference, traditional image features are often insufficient for accurate classification. In this study, an improved convolutional neural network based on YOLOv11 was proposed. Four beef muscles were categorized: sirloin (longissimus dorsi), fillet/tenderloin (psoas major), oyster blade (infraspinatus), and ribeye (longissimus thoracis). A dataset comprising 3598 images was established to support model training and validation. We divided the dataset into training, testing, and validation sets in a 6:2:2 ratio. To enhance model performance, wavelet convolution (WTConv) was employed to effectively expand the receptive field and improve image understanding, while a large separable kernel attention (LSKA) module was introduced to strengthen local feature representation and reduce background interference. Comparative experiments were conducted with other deep learning models as well as ablation tests to validate the proposed model’s effectiveness. Experimental results demonstrated that the proposed model achieved a classification accuracy of 98.50%, with Macro-Precision and Macro-Recall reaching 97.38% and 97.38%, respectively, and a detection speed of 147.66 FPS. These findings confirm the potential of the YOLOv11n-cls model for accurate beef classification and its practical application in intelligent meat recognition and processing within the Chinese beef industry.

Late-ripening citrus exhibits considerable spatiotemporal variation in fruit quality, and implementing zoned management is further complicated by the unique biological and ecological characteristics of the Sichuan Basin. This review provides the first integrated climate–purple soil–terrain synthesis of zoned management for late-ripening citrus in the Sichuan Basin, examined across four dimensions: driving mechanisms, technical approaches, methodological evolution, and limitations. First, the causes of spatiotemporal quality variation are clarified by considering three factors: climate, soil, and topography. Secondly, current monitoring, analysis, and zoned management technologies are assessed for suitability in the frequently cloudy and foggy environment, hilly topography, and scattered orchard plots of the Sichuan Basin. Additionally, the methodological evolution from “general fruit tree methods” to “citrus-specific methods” to “Sichuan scenario-adapted methods” is delineated, and five major research gaps are identified: spatiotemporal data fusion, universality of dynamic zoning, multi-factor interactive modeling, economic feasibility evaluation, and technology dissemination. Based on these findings, future research directions are proposed, including multi-source data fusion, dynamic modeling, cross-regional transferability, economic optimization, and lightweight inclusive solutions, to provide systematic research frameworks and methodological references for zoned management of late-ripening citrus in the Sichuan Basin.