The farming industry is facing the major challenge of intensive and inefficient harvesting labors. Thus, an efficient and automated fruit harvesting system is required. In this study, three object classification models based on Yolov5m integrated with BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs), respectively, are proposed for the automatic detection of tomato fruit. The various models were trained using 1508 normalized images containing three classes of cherry tomatoes, namely ripe, immature, and damaged. The detection accuracy for the three classes was found to be 94%, 95%, and 96%, respectively, for the modified Yolov5m + BoTNet model. The model thus appeared to provide a promising basis for the further development of automated harvesting systems for tomato fruit.

In this paper, we used two mass-produced industrial technologies, namely, thermal spraying and anodization methods, to enhance the surface characteristics of AISI 1045 medium carbon steel for use in special environments or products. The anodic film can effectively improve the surface properties of carbon steel. A sequence of treatments of the carbon steel substrate surface that consist of sandblasting, spraying the aluminum film, annealing, hot rolling, cleaning, grinding, and polishing can increase the quality of the anodized film. This paper proposes an anodization process for the surface of carbon steel to increase the corrosion resistance, hardness, color diversification, and electrical resistance. The resulting surface improves the hardness (from 170 HV to 524 HV), surface roughness (from 1.26 to 0.15 μm), coloring (from metal color to various colors), and corrosion resistance (from rusty to corrosion resistant). The electrochemical corrosion studies showed that the AISI 1045 steel surface with a hard anodized film had a lower corrosion current density of 10^−5.9 A/cm² and a higher impedance of 9000 ohm than those of naked AISI 1045 steel (10^−4.2 A/cm² and 150 ohm) in HCl gas.

Tea is one of the most consumed beverages in the world. However, tea plants are often susceptible to various diseases, especially leaf diseases. Currently, most tea farms identify leaf diseases through manual inspection. Due to its time-consuming and resource-intensive nature, manual inspection is impractical for large-scale applications. This study proposes a novel convolutional neural network model designated as YOLOv5 Bi-HIC for detecting tea leaf diseases, including algal leaf spot, tea brown blight, and tea grey blight. The model enhances the conventional YOLOv5 object detection model by incorporating instance and context information to improve the detection performance. A total of 1091 raw images of tea leaves affected by algal leaf spots, tea brown blight, and tea grey blight were captured at Wenhua Tea Farm, Miaoli City, Taiwan. The results indicate that the proposed model achieves precision, recall, F1 Score, and mAP values of 0.977, 0.943, 0.968, and 0.96, respectively, during training. Furthermore, it exhibits a detection confidence score of 0.94, 0.98, and 0.92 for algal leaf spot, tea brown blight, and tea grey blight, respectively. Overall, the results indicate that YOLOv5 Bi-HIC provides an accurate approach for real-time detection of leaf diseases and can serve as a valuable tool for timely intervention and management in tea plantations.