Explore the vast range of titles available.

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.

Wireless sensor network (WSN) is the key sensing resource for the internet of things (IoT) in vegetable greenhouse. The coverage control ensures that WSN can obtain enough effective information. However, the current coverage researches ignore the object size and lack of attention to the occlusion between targets. There are many leaves and fruits in vegetables, which can easily cause blind area and low utilization of directional sensors. Based on the geometric relationship between the directional sensors and targets, this paper studies a non-occlusion coverage scheme for the greenhouse IoT. Firstly, combined with the traditional coverage theory, a directional coverage model without occlusion is constructed by analysing the multivariate relationship between the sensor nodes and monitored targets. An objective function is then established to maximize the effective coverage. Based on the directional coverage model, this paper studies a hierarchical cooperative particle swarm optimization algorithm, which decomposes the global effective coverage problem into the utilization optimization of each sensor and finally get the orientation angle set. The experimental results show that the studied model and algorithm can avoid occlusion between covered objects while improving sensor utilization to a certain degree.

Precision weeding is crucial for maximizing crop yields and minimizing herbicide use. However, deploying standard deep learning models in agriculture faces challenges due to the high morphological diversity of weeds and the computational constraints of edge devices. Hence, this study proposes MCS-YOLO, a lightweight detection model based on the YOLOv8 architecture. First, a channel-level Mamba module is integrated into the backbone to model long-range feature dependencies and enhance global texture representation. The LMAB module employs parallel depthwise separable convolutions with varying receptive fields and coordinate attention to improve multi-scale weed discrimination. To mitigate feature blurring and misalignment during upsampling, the LCAU module adopts dynamic offset sampling beyond fixed interpolation methods. Finally, the SCS-Head integrates dual-branch depthwise separable convolution with channel shuffling to reduce parameter redundancy while preserving efficient feature expression. Experimental results on the Weed-Crop dataset demonstrate that MCS-YOLO achieves 76.4% mAP@50 and 38.3% mAP@50–95, outperforming YOLOv8s by 3.1% and 1.5%, respectively. Furthermore, the parameter count is reduced by 20.7%, from 11.13 M to 8.83 M, and GFLOPs are reduced by 39.6%, from 28.5 to 17.2. These results confirm that MCS-YOLO effectively balances a lightweight design with high detection accuracy, offering a viable solution for real-time weed detection and automated weeding on embedded agricultural platforms.

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 multimedia Internet of Things system is helpful for real-time monitoring and research of vegetable growth status and related environmental variables in the greenhouse. However, dense interleaved growth of vegetables can create a blind area of multimedia sensors. Different vegetable angle views also have different characteristics. The single-angle view cannot accurately obtain the concerned status information. The traditional multimedia sensor coverage mainly focuses on making the sensing region contain as many targets as possible, but the monitoring view and quality cannot be guaranteed due to the limited view angle and visual occlusion. Based on the actual needs, this paper studies an angle coverage judgment method based on the sensor set. By analyzing the topological relationship between each target and each corresponding sensor set, a multi-objective optimization function including angle coverage and area coverage is established, which can monitor the planting region from k angles. To solve this function, this paper then designs a two-layer code solution based on the traditional tabu search algorithm framework and adopts adaptive local search to improve the global search. Experimental results show that the judgement method in this paper is more efficient than other methods. The studied algorithm can converge to the excellent solution and obtain a small node set covering the target region from multiple angles as much as possible, thus improving the monitoring quality of vegetable greenhouse.

Papaver somniferum (opium poppy) is not only a source of raw material for the production of medical narcotic analgesics but also the major raw material for certain psychotropic drugs. Therefore, it is stipulated by law that the cultivation of Papaver somniferum must be authorized by the government under stringent supervision. In certain areas, unauthorized and illicit Papaver somniferum cultivation on private-owned lands occurs from time to time. These illegal Papaver somniferum cultivation sites are dispersedly-distributed and highly-concealed, therefore becoming a tough problem for government supervision. The low-altitude inspection of Papaver somniferum cultivation by unmanned aerial vehicles has the advantages of high efficiency and time saving, but the large amount of image data collected needs to be manually screened, which not only consumes a lot of manpower and material resources but also easily causes omissions. In response to the above problems, this paper proposed a two-stage (target detection and image classification) method for the detection of Papaver somniferum cultivation sites. In the first stage, the YOLOv5s algorithm was used to detect Papaver somniferum images for the purpose of identifying all the suspicious Papaver somniferum images from the original data. In the second stage, the DenseNet121 network was used to classify the detection results from the first stage, so as to exclude the targets other than Papaver somniferum and retain the images containing Papaver somniferum only. For the first stage, YOLOv5s achieved the best overall performance among mainstream target detection models, with a Precision of 97.7%, Recall of 94.9%, and mAP of 97.4%. For the second stage, DenseNet121 with pre-training achieved the best overall performance, with a classification accuracy of 97.33% and a Precision of 95.81%. The experimental comparison results between the one-stage method and the two-stage method suggest that the Recall of the two methods remained the same, but the two-stage method reduced the number of falsely detected images by 73.88%, which greatly reduces the workload for subsequent manual screening of remote sensing Papaver somniferum images. The achievement of this paper provides an effective technical means to solve the problem in the supervision of illicit Papaver somniferum cultivation.

To improve the production efficiency and reduce the labor cost of seedling operations, cabbage was selected as the research subject, and a novel approach based on the attention mechanism combining the deep convolutional neural network (DCNN) and long short-term memory (LSTM) is proposed. First, the cabbage growth data and environmental monitoring data were normalized, and input samples were obtained by sliding the time window. Then, the DCNN and the LSTM were used to extract the spatial feature information and temporal correlation of the samples, respectively. At the same time, the attention mechanism was used to set the weight coefficients of different feature information and highlight the role of the main features of the sample in the model, thereby improving the prediction accuracy. By analyzing the experimental data collected by the Shandong Seedling Plant, the DCNN-LSTM method based on the proposed attention mechanism achieved good prediction results, providing experience for the engineering application of decision-making regarding seedling transplanting time. The experimental data showed that the mean absolute error, root-mean-square error, mean absolute percentage error, and symmetric mean absolute percentage error of the prediction results of this method were 0.356, 0.507, 0.157, and 0.082, respectively. Compared with the CNN, LSTM, LSTM-Attention and CNN-LSTM models, this model showed higher prediction accuracy.

To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these challenges, this study explores a lightweight yet efficient algorithm for recognizing cabbage transplantation states in natural settings. Initially, FasterNet is integrated as the backbone network in the YOLOv5s model, aiming to expedite convergence speed and bolster feature extraction capabilities. Secondly, the introduction of the GAM attention mechanism enhances the algorithm’s focus on cabbage seedlings. EIoU loss is incorporated to improve both network convergence speed and localization precision. Lastly, the model incorporates deformable convolution DCNV3, which further optimizes model parameters and attains a superior balance between accuracy and speed. Upon testing the refined YOLOv5s target detection algorithm, improvements were evident. When compared to the original model, the mean average precision (mAP) rose by 3.5 percentage points, recall increased by 1.7 percentage points, and detection speed witnessed an impressive boost of 52 FPS. This enhanced algorithm not only reduces model complexity but also elevates network performance. The method is expected to streamline transplantation quality measurements, minimize time and labor inputs, and elevate field transplantation quality surveys’ automation levels.

Wireless sensor networks (WSN) enable the acquisition of multisource environmental data and crop states in precision agriculture. However, the complex agricultural environment causes the WSN topology to change frequently and link connection probability is difficult to predict. In order to improve the utilization of network resources and balance the network energy consumption, this paper studies an agricultural fault-tolerant topology construction method based on the potential game and cut vertex detection. Considering the connectivity redundancy, node lifetime, and residual energy, a fault-tolerant topology algorithm for agricultural WSN based on a double price function is designed. The network is clustered according to the node location and residual energy to form a single-hop effective cluster. Based on the network cluster, the price function is constructed in order to reduce energy consumption and balance network energy efficiency. The initial transmit power set supporting inter-cluster communication is obtained by potential game theory. While preserving the game characteristics of topology, the redundant links are eliminated and the transmit power is adjusted by a cut vertex detection algorithm to realize the construction of a 2-connected cluster head network. Simulation results show that the network topology constructed by the studied algorithm can balance the energy consumption and prolong the network lifetime effectively.

Transporting harvested vegetables in the field or greenhouse is labor-intensive. The utilization of small harvest-aid vehicles can reduce non-productive time for farmers and improve harvest efficiency. This paper models the process of harvesting vegetables in response to non-productive waiting delays caused by the scheduling of harvest-aid vehicles. Taking into consideration harvesting speed, harvest-aid vehicle capacity, and scheduling conflicts, a harvest-aid vehicle scheduling model is constructed to minimize non-production waiting time and coordination costs. Subsequently, to meet the collaborative needs of harvesters, this paper develops a discrete multi-objective Jaya optimization algorithm (DMO-Jaya), which combines an opposition-based learning mechanism and a long-term memory library to obtain scheduling schemes suitable for agricultural environments. Experiments show that the studied model can schedule harvest-aid vehicles without conflicts. Compared to the NSGA-II algorithm and the MMOPSO, the DMO-Jaya algorithm demonstrates a better diversity of solutions, resulting in a shorter non-productive waiting time for harvesters. This research provides a reference model for improving the efficiency of vegetable harvesting and transportation.