China is the world’s leading producer of apples. However, the current classification of apple maturity is predominantly reliant on manual expertise, a process that is both inefficient and costly. In this study, we utilize a diverse array of apples of varying ripeness levels as the research subjects. We propose a lightweight target detection model, termed BGWL-YOLO, which is based on YOLOv11n and incorporates the following specific improvements. To enhance the model’s ability for multi-scale feature fusion, a bidirectional weighted feature pyramid network (BiFPN) is introduced in the neck. In response to the problem of redundant computation in convolutional neural networks, a GhostConv is used to replace the standard convolution. The Wise-Inner-MPDIoU (WIMIoU) loss function is introduced to improve the localization accuracy of the model. Finally, the LAMP pruning algorithm is utilized to further compress the model size. The experimental results demonstrate that the BGWL-YOLO model attains a detection and recognition precision rate of 83.5%, a recall rate of 81.7%, and an average precision mean of 90.1% on the test set. A comparative analysis reveals that the number of parameters has been reduced by 65.3%, the computational demands have been decreased by 57.1%, the frames per second (FPS) have been boosted by 5.8% on the GPU and 32.8% on the CPU, and most notably, the model size has been reduced by 74.8%. This substantial reduction in size is highly advantageous for deployment on compact smart devices, thereby facilitating the advancement of smart agriculture.

The removal of pineapple eyes is a crucial step in pineapple processing. However, their irregularly distributed spiral arrangement presents a dual challenge for positioning accuracy and automated removal by the end-effector. In order to solve this problem, a pineapple eye removal device based on machine vision was designed. The device comprises a clamping mechanism, an eye removal end-effector, an XZ two-axis sliding table, a depth camera, and a control system. Taking the eye removal time and rotational angular velocity as variables, the relationship between the rod length of the prime mover and the contact force and gear torque during the eye removal process was simulated and analyzed using ADAMS (2020) software. Based on these simulations, the optimal length of the prime mover for the end-effector was determined to be 23.00 mm. The performance of various YOLOv5 models was compared in terms of accuracy, recall rate, mean detection error, and detection time. The YOLOv5s model was chosen for real-time pineapple eye detection, and the eye’s position was determined through coordinate transformation. The control system then actuated the XZ two-axis sliding table to position the eye removal end-effector for effective removal. The results indicated an average complete removal rate of 88.5%, an incomplete removal rate of 6.6%, a missed detection rate of 4.9%, and an average removal time of 156.7 s per pineapple. Compared with existing solutions, this study optimized the end-effector design for pineapple eye removal. Depth information was captured with a depth camera, and machine vision was combined with three-dimensional localization. These steps improved removal accuracy and increased production efficiency.

The structural parameters of a grain dryer are directly related to the its energy consumption and quality formation. Therefore, based on the Ergun model, air state parameters and uniformity evaluation method, the effect of changes in the ventilation area on grain airflow resistance, drying energy consumption, drying efficiency and uniformity are theoretically and experimentally studied in this work under the same initial grain weight and air flux. The results show that under the conditions of air temperatures 35℃ and 70℃, the weight of paddy 8.547 kg and the air flux 12.3 m³·h^-1, hot air introduced into the drying chamber with cross-sectional areas of S1 and S2 respectively, the ventilation area enlarged by 2.328 times, the grain airflow resistance decreased by 7.17 and 6.99 times. Enlarging the ventilation area effectively improved the drying rate of paddy, especially at 70℃, while the unit energy consumption was the opposite. It also accelerated the moving speed of the saturated humidity line in the drying layer and improved the drying uniformity of the paddy. These experimental results are in agreement with the theoretical analysis, which provides a reference for the design of grain drying equipment and technology.

Rice bacterial leaf blight (BLB), caused by the bacterium Xanthomonas oryzae pv. oryzae, severely damages leaves during rice growth, leading to reduced yield or even death. This study aimed to develop an automated identification and assessment method for rice BLB based on multispectral UAV imagery to overcome the limitations of in-field inspection methods. By obtaining multispectral image data of rice fields and extracting color features (CFs), texture features (TFs), and vegetation indices (VIs) of rice canopy using image processing techniques, three algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Back Propagation Neural Network (BPNN), were utilized to establish a monitoring model for the severity levels of rice BLB. The classification results of several models are compared, with the overall Correct Identification Rate (CIR) of the three-feature fusion classification algorithm generally higher than the other two. Among the three algorithms, the RF algorithm performs the best, with a CIR reaching 93.4% and a Kappa coefficient of 0.91. The BPNN algorithm follows, with a CIR of 82.1% and a Kappa coefficient of 0.76, showing moderate effectiveness. Lastly, the SVM algorithm performs the poorest, with a CIR of 65.1% and a Kappa coefficient of 0.54. A rice BLB large-scale detection framework based on unmanned aerial vehicle (UAV) images was designed, and a graphical user interface (GUI) was developed using Python language to achieve automated processing from image input to final recognition results, achieving good results.