Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported.

Lodging is one of the major issues that seriously affects wheat quality and yield. To obtain timely and accurate wheat lodging information and identify the potential factors leading to lodged wheat in wheat breeding programs, we proposed a lodging-detecting model coupled with unmanned aerial vehicle (UAV) image features of wheat at multiple plant growth stages. The UAV was used to collect canopy images and ground lodging area information at five wheat growth stages. The PSPNet model was improved by combining the convolutional LSTM (ConvLSTM) timing model, inserting the convolutional attention module (CBAM) and the Tversky loss function. The effect of the improved PSPNet network model in monitoring wheat lodging under different image sizes and different growth stages was investigated. The experimental results show that (1) the improved Lstm_PSPNet model was more effective in lodging prediction, and the precision reached 0.952; (2) choosing an appropriate image size could improve the segmentation accuracy, with the optimal image size in this study being 468 × 468; and (3) the model of Lstm_PSPNet improved its segmentation accuracy sequentially from early flowering to late maturity, and the three evaluation metrics increased sequentially from 0.932 to 0.952 for precision, from 0.912 to 0.940 for recall, and from 0.922 to 0.950 for F1-Score, with good extraction at mid and late reproductive stages. Therefore, the lodging information extraction model proposed in this study can make full use of temporal sequence features to improve image segmentation accuracy and effectively extract lodging areas at different growth stages. The model can provide more comprehensive reference and technical support for monitoring the lodging of wheat crops at different growth stages.

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology.

In the field of ceramic product defect detection, traditional manual visual inspection methods suffer from low efficiency and high subjectivity, while existing deep learning algorithms are limited in detection efficiency due to their high complexity. To address these challenges, this study proposes a deep learning-based algorithm for ceramic product defect detection. The algorithm designs a lightweight YOLOv10s detector, which reconstructs the backbone network using GhostNet and incorporates an Efficient Channel Attention (ECA) mechanism fused with depthwise separable convolutions, effectively reducing the model’s complexity and computational load. Additionally, an adaptive threshold method is proposed to improve the traditional Canny edge detection algorithm, significantly enhancing its accuracy in defect edge detection. Experimental results demonstrate that the algorithm achieves an mAP@50 of 92.8% and an F1-score of 90.3% in ceramic product defect detection tasks, accurately identifying and locating four types of defects: cracks, glaze missing, damage, and black spots. In crack detection, the average Edge Localization Error (ELE) is reduced by 25%, the Edge Connectivity Rate (ECR) is increased by 15%, the Weak Edge Responsiveness (WER) is improved by 17%, and the frame rate reaches 40 frames per second (f/s), meeting real-time detection requirements. This algorithm exhibits significant potential in the field of ceramic product defect detection, providing solid technical support for optimizing the ceramic product manufacturing process.

After reviewing a large number of books and articles on the cost of tobacco logistics, this paper summarizes the results of the study on tobacco logistics costs. First of all, it summarizes the characteristics of tobacco logistics, which leads out the development trend of tobacco and the development of logistics cost. Combined with the research and application of system dynamics method in logistics field, the development and importance of tobacco logistics cost based on system dynamics are discussed and demonstrated in many aspects. Finally, the problems of tobacco logistics are summarized from the aspects of construction, important link, scale and management of tobacco logistics. After comparing the method of system dynamics, the advantages and importance of the latter are drawn, and made a vision and thinking about how to control the cost of tobacco logistics in the future.