Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

Tomato is affected by various diseases and pests during its growth process. If the control is not timely, it will lead to yield reduction or even crop failure. How to control the diseases and pests effectively and help the vegetable farmers to improve the yield of tomato is very important, and the most important thing is to accurately identify the diseases and insect pests. Compared with the traditional pattern recognition method, the diseases and pests recognition method based on deep learning can directly input the original image. Instead of the tedious steps such as image preprocessing, feature extraction and feature classification in the traditional method, the end-to-end structure is adopted to simplify the recognition process and solve the problem that the feature extractor designed manually is difficult to obtain the feature expression closest to the natural attribute of the object. Based on the application of deep learning object detection, not only can save time and effort, but also can achieve real-time judgment, greatly reduce the huge loss caused by diseases and pests, which has important research value and significance. Based on the latest research results of detection theory based on deep learning object detection and the characteristics of tomato diseases and pests images, this study will build the dataset of tomato diseases and pests under the real natural environment, optimize the feature layer of Yolo V3 model by using image pyramid to achieve multi-scale feature detection, improve the detection accuracy and speed of Yolo V3 model, and detect the location and category of diseases and pests of tomato accurately and quickly. Through the above research, the key technology of tomato pest image recognition in natural environment is broken through, which provides reference for intelligent recognition and engineering application of plant diseases and pests detection.

Background: Tomato gray leaf spot is a worldwide disease, especially in warm and humid areas. The continuous expansion of greenhouse tomato cultivation area and the frequent introduction of foreign varieties in recent years have increased the severity of the epidemic hazards of this disease in some tomato planting bases annually. This disease is a newly developed one. Thus, farmers generally lack prevention and control experience and measures in production; the disease is often misdiagnosed or not prevented and controlled timely; this condition results in tomato production reduction or crop failure, which causes severe economic losses to farmers. Therefore, tomato gray leaf spot disease should be identified in the early stage, which will be important in avoiding or reducing the economic loss caused by the disease. The advent of the era of big data has facilitated the use of machine learning method in disease identification. Therefore, deep learning method is proposed to realise the early recognition of tomato gray leaf spot. Tomato growers need to develop the app of image detection mobile terminal of tomato gray leaf spot disease to realise real-time detection of this disease. Results: This study proposes an early recognition method of tomato leaf spot based on MobileNetv2-YOLOv3 model to achieve a good balance between the accuracy and real-time detection of tomato gray leaf spot. This method improves the accuracy of the regression box of tomato gray leaf spot recognition by introducing the GIoU bounding box regression loss function. A MobileNetv2-YOLOv3 lightweight network model, which uses MobileNetv2 as the backbone network of the model, is proposed to facilitate the migration to the mobile terminal. The pre-training method combining mixup training and transfer learning is used to improve the generalisation ability of the model. The images captured under four different conditions are statistically analysed. The recognition effect of the models is evaluated by the F1 score and the AP value, and the experiment is compared with Faster-RCNN and SSD models. Experimental results show that the recognition effect of the proposed model is significantly improved. In the test dataset of images captured under the background of sufficient light without leaf shelter, the F1 score and AP value are 94.13% and 92.53%, and the average IOU value is 89.92%. In all the test sets, the F1 score and AP value are 93.24% and 91.32%, and the average IOU value is 86.98%. The object detection speed can reach 246 frames/s on GPU, the extrapolation speed for a single 416 × 416 picture is 16.9 ms, the detection speed on CPU can reach 22 frames/s, the extrapolation speed is 80.9 ms and the memory occupied by the model is 28 MB. Conclusions: The proposed recognition method has the advantages of low memory consumption, high recognition accuracy and fast recognition speed. This method is a new solution for the early prediction of tomato leaf spot and a new idea for the intelligent diagnosis of tomato leaf spot.

Background: Research on early object detection methods of crop diseases and pests in the natural environment has been an important research direction in the fields of computer vision, complex image processing and machine learning. Because of the complexity of the early images of tomato diseases and pests in the natural environment, the traditional methods can not achieve real-time and accurate detection.Results: Aiming at the complex background of early period of tomato diseases and pests image objects in the natural environment, an improved object detection algorithm based on YOLOv3 for early real-time detection of tomato diseases and pests was proposed. Firstly, aiming at the complex background of tomato diseases and pests images under natural conditions, dilated convolution layer is used to replace convolution layer in backbone network to maintain high resolution and receptive field and improve the ability of small object detection. Secondly, in the detection network, according to the size of candidate box intersection ratio (IOU) and linear attenuation confidence score predicted by multiple grids, the obscured objects of tomato diseases and pests are retained, and the detection problem of mutual obscure objects of tomato diseases and pests is solved. Thirdly, to reduce the model volume and reduce the model parameters, the network is lightweight by using the idea of convolution factorization. Finally, by introducing a balance factor, the small object weight in the loss function is optimized. The test results of nine common tomato diseases and pests under six different background conditions are statistically analyzed. The proposed method has a F1 value of 94.77%, an AP value of 91.81%, a false detection rate of only 2.1%, and a detection time of only 55 Ms. The test results show that the method is suitable for early detection of tomato diseases and pests using large-scale video images collected by the agricultural Internet of Things.Conclusions: At present, most of the object detection of diseases and pests based on computer vision needs to be carried out in a specific environment (such as picking the leaves of diseases and pests and placing them in the environment with light supplement equipment, so as to achieve the best environment). For the images taken by the Internet of things monitoring camera in the field, due to various factors such as light intensity, weather change, etc., the images are very different, the existing methods cannot work reliably. The proposed method has been applied to the actual tomato production scenarios, showing good detection performance. The experimental results show that the method in this study improves the detection effect of small objects and leaves occlusion, and the recognition effect under different background conditions is better than the existing object detection algorithms. The results show that the method is feasible to detect tomato diseases and pests in the natural environment.

This study introduces YOLOv8n-vegetable, a model designed to address challenges related to imprecise detection of vegetable diseases in greenhouse plant environment using existing network models. The model incorporates several improvements and optimizations to enhance its effectiveness. Firstly, a novel C2fGhost module replaces partial C2f. with GhostConv based on Ghost lightweight convolution, reducing the model’s parameters and improving detection performance. Second, the Occlusion Perception Attention Module (OAM) is integrated into the Neck section to better preserve feature information after fusion, enhancing vegetable disease detection in greenhouse settings. To address challenges associated with detecting small-sized objects and the depletion of semantic knowledge due to varying scales, an additional layer for detecting small-sized objects is included. This layer improves the amalgamation of extensive and basic semantic knowledge, thereby enhancing overall detection accuracy. Finally, the HIoU boundary loss function is introduced, leading to improved convergence speed and regression accuracy. These improvement strategies were validated through experiments using a self-built vegetable disease detection dataset in a greenhouse environment. Multiple experimental comparisons have demonstrated the model's effectiveness, achieving the objectives of improving detection speed while maintaining accuracy and real-time detection capability. According to experimental findings, the enhanced model exhibited a 6.46% rise in mean average precision (mAP) over the original model on the self-built vegetable disease detection dataset under greenhouse conditions. Additionally, the parameter quantity and model size decreased by 0.16G and 0.21 MB, respectively. The proposed model demonstrates significant advancements over the original algorithm and exhibits strong competitiveness when compared with other advanced object detection models. The lightweight and fast detection of vegetable diseases offered by the proposed model presents promising applications in vegetable disease detection tasks.

Tomatoes possess significant nutritional and economic value. However, frequent diseases can detrimentally impact their quality and yield. Images of tomato diseases captured amidst intricate backgrounds are susceptible to environmental disturbances, presenting challenges in achieving precise detection and identification outcomes. This study focuses on tomato disease images within intricate settings, particularly emphasizing four prevalent diseases (late blight, gray leaf spot, brown rot, and leaf mold), alongside healthy tomatoes. It addresses challenges such as excessive interference, imprecise lesion localization for small targets, and heightened false-positive and false-negative rates in real-world tomato cultivation settings. To address these challenges, we introduce a novel method for tomato disease detection named TomatoDet. Initially, we devise a feature extraction module integrating Swin-DDETR’s self-attention mechanism to craft a backbone feature extraction network, enhancing the model’s capacity to capture details regarding small target diseases through self-attention. Subsequently, we incorporate the dynamic activation function Meta-ACON within the backbone network to further amplify the network’s ability to depict disease-related features. Finally, we propose an enhanced bidirectional weighted feature pyramid network (IBiFPN) for merging multi-scale features and feeding the feature maps extracted by the backbone network into the multi-scale feature fusion module. This enhancement elevates detection accuracy and effectively mitigates false positives and false negatives arising from overlapping and occluded disease targets within intricate backgrounds. Our approach demonstrates remarkable efficacy, achieving a mean Average Precision (mAP) of 92.3% on a curated dataset, marking an 8.7% point improvement over the baseline method. Additionally, it attains a detection speed of 46.6 frames per second (FPS), adeptly meeting the demands of agricultural scenarios.