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Background: In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise. Results: Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model. Conclusion: The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions: (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment. [ABSTRACT FROM AUTHOR]

Uneven illumination, obstruction of leaves or branches, and the overlapping of fruit significantly affect the accuracy of tomato detection by automated harvesting robots in natural environments. In this study, a proficient and accurate algorithm for tomato detection, called SBCS-YOLOv5s, is proposed to address this practical challenge. SBCS-YOLOv5s integrates the SE, BiFPN, CARAFE and Soft-NMS modules into YOLOv5s to enhance the feature expression ability of the model. First, the SE attention module and the C3 module were combined to form the C3SE module, replacing the original C3 module within the YOLOv5s backbone architecture. The SE attention module relies on modeling channel-wise relationships and adaptive re-calibration of feature maps to capture important information, which helps improve feature extraction of the model. Moreover, the SE module's ability to adaptively re-calibrate features can improve the model's robustness to variations in environmental conditions. Next, the conventional PANet multi-scale feature fusion network was replaced with an efficient, weighted Bi-directional Feature Pyramid Network (BiFPN). This adaptation aids the model in determining useful weights for the comprehensive fusion of high-level and bottom-level features. Third, the regular up-sampling operator is replaced by the Content Aware Reassembly of Features (CARAFE) within the neck network. This implementation produces a better feature map that encompasses greater semantic information. In addition, CARAFE's ability to enhance spatial detail helps the model discriminate between closely spaced fruits, especially for tomatoes that overlap heavily, potentially reducing the number of merging detections. Finally, for heightened identification of occluded and overlapped fruits, the conventional Non-Maximum-Suppression (NMS) algorithm was substituted with the Soft-NMS algorithm. Since Soft-NMS adopts a continuous weighting scheme, it is more adaptable to varying object sizes, improving the handling of small or large fruits in the image. Remarkably, this is carried out without introducing changes to the computational complexity. The outcome of the experiments showed that SBCS-YOLOv5s achieved a mean average precision (mAP (0.5:0.95)) of 87.7%, which is 3.5% superior to the original YOLOv5s model. Moreover, SBCS-YOLOv5s has a detection speed of 2.6 ms per image. Compared to other state-of-the-art detection algorithms, SBCS-YOLOv5s performed the best, showing tremendous promise for tomato detection in natural environments., Competing Interests: Author GL was employed by the company Univalsoft Joint Stock Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Touko Mbouembe, Liu, Park and Kim.)

Automatic and accurate detection of fruit in greenhouse is challenging due to complicated environment conditions. Leaves or branches occlusion, illumination variation, overlap and cluster between fruits make the fruit detection accuracy to decrease. To address this issue, an accurate and robust fruit-detection algorithm was proposed for tomato detection based on an improved YOLOv4-tiny model. First, an improved backbone network was used to enhance feature extraction and reduce overall computational complexity. To obtain the improved backbone network, the BottleneckCSP modules of the original YOLOv4-tiny backbone were replaced by a Bottleneck module and a reduced version of BottleneckCSP module. Then, a tiny version of CSP-Spatial Pyramid Pooling (CSP-SPP) was attached to the new backbone network to improve the receptive field. Finally, a Content Aware Reassembly of Features (CARAFE) module was used in the neck instead of the traditional up-sampling operator to obtain a better feature map with high resolution. These modifications improved the original YOLOv4-tiny and helped the new model to be more efficient and accurate. The experimental results showed that the precision, recall, F 1 score, and the mean average precision (mAP) with Intersection over Union (IoU) of 0.5 to 0.95 were 96.3%, 95%, 95.6%, and 82.8% for the improved YOLOv4-tiny model, respectively. The detection time was 1.9 ms per image. The overall detection performance of the improved YOLOv4-tiny was better than that of state-of-the-art detection methods and met the requirements of tomato detection in real time., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mbouembe, Liu, Sikati, Kim and Kim.)

The accurate and robust detection of fruits in the greenhouse is a critical step of automatic robot harvesting. However, the complicated environmental conditions such as uneven illumination, leaves or branches occlusion, and overlap between fruits make it difficult to develop a robust fruit detection system and hinders the step of commercial application of harvesting robots. In this study, we propose an improved anchor-free detector called TomatoDet to deal with the above challenges. First, an attention mechanism is incorporated into the CenterNet backbone to improve the feature expression ability. Then, a circle representation is introduced to optimize the detector to make it more suitable for our specific detection task. This new representation can not only reduce the degree of freedom for shape fitting, but also simplifies the regression process from detected keypoints. The experimental results showed that the proposed TomatoDet outperformed other state-of-the-art detectors in respect of tomato detection. The F 1 score and average precision of TomatoDet reaches 95.03 and 98.16%. In addition, the proposed detector performs robustly under the condition of illumination variation and occlusion, which shows great promise in tomato detection in the greenhouse., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Hou, Liu, Liu, Zhao and Li.)

Tomato plants are infected by diseases and insect pests in the growth process, which will lead to a reduction in tomato production and economic benefits for growers. At present, tomato pests are detected mainly through manual collection and classification of field samples by professionals. This manual classification method is expensive and time-consuming. The existing automatic pest detection methods based on a computer require a simple background environment of the pests and cannot locate pests. To solve these problems, based on the idea of deep learning, a tomato pest identification algorithm based on an improved YOLOv4 fusing triplet attention mechanism (YOLOv4-TAM) was proposed, and the problem of imbalances in the number of positive and negative samples in the image was addressed by introducing a focal loss function. The K-means + + clustering algorithm is used to obtain a set of anchor boxes that correspond to the pest dataset. At the same time, a labeled dataset of tomato pests was established. The proposed algorithm was tested on the established dataset, and the average recognition accuracy reached 95.2%. The experimental results show that the proposed method can effectively improve the accuracy of tomato pests, which is superior to the previous methods. Algorithmic performance on practical images of healthy and unhealthy objects shows that the proposed method is feasible for the detection of tomato pests., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Wang, Miao and Liu.)