Considering that the occurrence and spread of diseases are closely related to the planting environment, a tomato disease diagnosis method based on Multi-ResNet34 multi-modal fusion learning based on residual learning is proposed for the problem of limited recognition rate of a single RGB image of a tomato disease. Based on the ResNet34 backbone network, this paper introduces transfer learning to speed up training, reduce data dependencies, and prevent overfitting due to a small amount of sample data; it also integrates multi-source data (tomato disease image data and environmental parameters). The feature-level multi-modal data fusion method is used to retain the key information of the data to identify the feature, so that the different modal data can complement, support and correct each other, and obtain a more accurate identification effect. Firstly, Mask R-CNN was used to extract partial images of leaves from complex background tomato disease images to reduce the influence of background regions on disease identification. Then, the formed image environment data set was input into the multi-modal fusion model to obtain the identification results of disease types. The proposed multi-modal fusion model Multi-ResNet34 has a classification accuracy of 98.9% for six tomato diseases: bacterial spot, late blight, leaf mold, yellow aspergillosis, gray mold, and early blight, which is higher than that of the single-modal model. With the increase by 1.1%, the effect is obvious. The method in this paper can provide an important basis for the analysis and diagnosis of tomato intelligent greenhouse diseases in the context of agricultural informatization.

The labor shortage and rising costs in the greenhouse industry have driven the development of automation, with the core of autonomous operations being positioning and navigation technology. However, precise positioning in complex greenhouse environments and narrow aisles poses challenges to localization technologies. This study proposes a multi-sensor fusion positioning and navigation robot based on ultra-wideband (UWB), an inertial measurement unit (IMU), odometry (ODOM), and a laser rangefinder (RF). The system introduces a confidence optimization algorithm based on weakening non-line-of-sight (NLOS) for UWB positioning, obtaining calibrated UWB positioning results, which are then used as a baseline to correct the positioning errors generated by the IMU and ODOM. The extended Kalman filter (EKF) algorithm is employed to fuse multi-sensor data. To validate the feasibility of the system, experiments were conducted in a Chinese solar greenhouse. The results show that the proposed NLOS confidence optimization algorithm significantly improves UWB positioning accuracy by 60.05%. At a speed of 0.1 m/s, the root mean square error (RMSE) for lateral deviation is 0.038 m and for course deviation is 4.030°. This study provides a new approach for greenhouse positioning and navigation technology, achieving precise positioning and navigation in complex commercial greenhouse environments and narrow aisles, thereby laying a foundation for the intelligent development of greenhouses.

Poppy is a special medicinal plant. Its cultivation requires legal approval and strict supervision. Unauthorized cultivation of opium poppy is forbidden. Low-altitude inspection of poppy illegal cultivation through unmanned aerial vehicle is featured with the advantages of time-saving and high efficiency. However, a large amount of inspection image data collected need to be manually screened and analyzed. This process not only consumes a lot of manpower and material resources, but is also subjected to omissions and errors. In response to such a problem, this paper proposed an inspection method by adding a larger-scale detection box on the basis of the original YOLOv3 algorithm to improve the accuracy of small target detection. Specifically, ResNeXt group convolution was utilized to reduce the number of model parameters, and an ASPP module was added before the small-scale detection box to improve the model’s ability to extract local features and obtain contextual information. The test results on a self-created dataset showed that: the mAP (mean average precision) indicator of the Global Multiscale-YOLOv3 model was 0.44% higher than that of the YOLOv3 (MobileNet) algorithm; the total number of parameters of the proposed model was only 13.75% of that of the original YOLOv3 model and 35.04% of that of the lightweight network YOLOv3 (MobileNet). Overall, the Global Multiscale-YOLOv3 model had a reduced number of parameters and increased recognition accuracy. It provides technical support for the rapid and accurate image processing in low-altitude remote sensing poppy inspection.

In view of the differences in appearance and the complex backgrounds of crop diseases, automatic identification of field diseases is an extremely challenging topic in smart agriculture. To address this challenge, a popular approach is to design a Deep Convolutional Neural Network (DCNN) model that extracts visual disease features in the images and then identifies the diseases based on the extracted features. This approach performs well under simple background conditions, but has low accuracy and poor robustness under complex backgrounds. In this paper, an end-to-end disease identification model composed of a disease-spot region detector and a disease classifier (YOLOv5s + BiCMT) was proposed. Specifically, the YOLOv5s network was used to detect the disease-spot regions so as to provide a regional attention mechanism to facilitate the disease identification task of the classifier. For the classifier, a Bidirectional Cross-Modal Transformer (BiCMT) model combining the image and text modal information was constructed, which utilizes the correlation and complementarity between the features of the two modalities to achieve the fusion and recognition of disease features. Meanwhile, the problem of inconsistent lengths among different modal data sequences was solved. Eventually, the YOLOv5s + BiCMT model achieved the optimal results on a small dataset. Its Accuracy, Precision, Sensitivity, and Specificity reached 99.23, 97.37, 97.54, and 99.54%, respectively. This paper proves that the bidirectional cross-modal feature fusion by combining disease images and texts is an effective method to identify vegetable diseases in field environments.

Transplanting is a critical step in vegetable production, and the application of automatic transplanters can significantly reduce labor intensity, improve production efficiency, and enhance the precision and consistency of operations. However, automatic transplanters are structurally complex, with diverse components, each design and function offering its own advantages and limitations. To assist industry professionals in quickly understanding and selecting transplanters suited to specific crops and environments, this paper reviews three key technologies in current vegetable transplanters: planting mechanisms, automated seedling picking and placing, and tray conveyance. Each technology is classified, compared, and analyzed to evaluate its applicability. Based on the current state of technology, the paper identifies major challenges in the development of vegetable transplanters in China, including insufficient integration of machinery and agronomy, high demands for equipment adaptability, lack of standardized systems, and delays in the development of core technologies for fully automated transplanting. Solutions are proposed for each of these issues. Finally, the paper discusses future directions for the development of automatic transplanters, including enhancing transplanting efficiency, achieving autonomous navigation, digitalizing operations, developing supporting systems for transplanting, and unmanned transplanting.

The development of resistant cucumber varieties is of a great importance for reducing the production loss caused by root-knot nematodes. After cucumber plants are infected with root-knot nematodes, their roots will swell into spherical bumps. Rapid and accurate detection of the infected sites and assessment of the disease severity play a key role in selecting resistant cucumber varieties. Because the locations and sizes of the spherical bumps formed after different degrees of infection are random, the currently available detection and counting methods based on manual operation are extremely time-consuming and labor-intensive, and are prone to human error. In response to these problems, this paper proposes a cucumber root-knot nematode detection model based on the modified YOLOv5s model (i.e., YOLOv5-CMS) in order to support the breeding of resistant cucumber varieties. In the proposed model, the dual attention module (CBAM-CA) was adopted to enhance the model’s ability of extracting key features, the K-means++ clustering algorithm was applied to optimize the selection of the initial cluster center, which effectively improved the model’s performance, and a novel bounding box regression loss function (SIoU) was used to fuse the direction information between the ground-truth box and the predicted box so as to improve the detection precision. The experiment results show that the recall (R) and mAP of the YOLOv5s-CMS model were improved by 3% and 3.1%, respectively, compared to the original YOLOv5s model, which means it can achieve a better performance in cucumber root-knot nematode detection. This study provides an effective method for obtaining more intuitive and accurate data sources during the breeding of cucumber varieties resistant to root-knot nematode.