In recent years, automatic weed control has emerged as a promising alternative for reducing the amount of herbicide applied to the field, instead of conventional spraying. This method is beneficial to reduce environmental pollution and to achieve sustainable agricultural development. Achieving a rapid and accurate detection of weeds in maize seedling stage in natural environments is the key to ensuring maize yield and the development of automatic weeding machines. Based on the lightweight YOLO v4-tiny model, a maize weed detection model which combined an attention mechanism and a spatial pyramid pooling structure was proposed. To verify the effectiveness of the proposed method, five different deep-learning algorithms, including the Faster R-CNN, the SSD 300, the YOLO v3, the YOLO v3-tiny, and the YOLO v4-tiny, were compared to the proposed method. The comparative results showed that the mAP (Mean Average Precision) of maize seedlings and its associated weed detection using the proposed method was 86.69%; the detection speed was 57.33 f/s; and the model size was 34.08 MB. Furthermore, the detection performance of weeds under different weather conditions was discussed. The results indicated that the proposed method had strong robustness to the changes in weather, and it was feasible to apply the proposed method for the real-time and accurate detection of weeds.

Maize leaf disease identification using deep transfer convolutional neural networks

With the development and progress of uncrewed farming technology, uncrewed rice transplanters have gradually become an indispensable part of modern agricultural production; however, in the actual production, the working quality of uncrewed rice transplanters have not been effectively detected. In order to solve this problem, a detection method of uncrewed transplanter omission is proposed in this paper. In this study, the RGB images collected in the field were inputted into a convolutional neural network, and the bounding box center of the network output was used as the approximate coordinates of the rice seedlings, and the horizontal and vertical crop rows were fitted by the least square method, so as to detect the phenomenon of rice omission. By adding atrous spatial pyramid pooling and a convolutional block attention module to YOLOv5, the problem of image distortion caused by scaling and cropping is effectively solved, and the recognition accuracy is improved. The accuracy of this method is 95.8%, which is 5.6% higher than that of other methods, and the F1-score is 93.39%, which is 4.66% higher than that of the original YOLOv5. Moreover, the network structure is simple and easy to train, with the average training time being 0.284 h, which can meet the requirements of detection accuracy and speed in actual production. This study provides an effective theoretical basis for the construction of an uncrewed agricultural machinery system.

Three-dimensional(3D) seedling reconstruction technology can provide critical technical support for monitoring plant growth, phenotyping high-throughput plants, and conducting precision agriculture. However, multiview image-based reconstruction methods, which rely on image registration and feature matching, are susceptible to issues such as similar textures and viewpoint differences, leading to matching errors and the loss of key structural information. This can result in local deficiencies and reduced accuracy in the reconstructed models. Therefore, to attain improved reconstruction accuracy under low-cost constraints, deep learning-based feature extraction and matching methods are employed in this study, the SuperPoint network is utilized to increase the robustness of the feature point detection and description processes, and the LightGlue algorithm is introduced to improve the accuracy and stability of matching. Additionally, to reduce the impact of shooting and platform jitter on image quality, a dedicated plant 3D reconstruction platform is designed and constructed, and a dataset of densely planted rice seedlings under light stress conditions is collected, comprising three factors (light quality, light quantity, and the photoperiod) × three levels, totaling nine groups. Experimental results show that the proposed method achieves optimal performance in terms of its point cloud completeness and reprojection error. The phenotypic parameters (e.g., plant height) extracted from the reconstruction data are strongly correlated with the actual measurements (R² = 0.989, RMSE = 4.54 mm), validating the potential of the proposed method for applications related to simulating plant growth processes, analyzing the effects of environmental factors (e.g., light), and optimizing crop cultivation schemes.