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• An efficient method is proposed for orchard tree trunk detection in natural scenes. • A multi-scenes orchard tree trunk dataset is established for experiments. • The YOLOv5 model structure is improved to enhance detection accuracy and speed. • Proposed method is robust against complex orchard environment. The recognition and localization of fruit tree trunks in orchard are important for orchard operation robots, which are the bases for automatic navigation, fruit tree spraying and fertilization etc. A method was proposed based on machine vision to detect target objects such as fruit tree trunks, person and supporters in orchard by improving the YOLOv5 deep learning algorithm in this paper, which is applicable to the recognition tasks in natural orchard environments. Firstly, 1354 images of the natural orchard collected by camera were image enhanced, weather effects such as rain, snow, bright light, shadow and fog were added to expand the dataset and to increase the robustness of the model. Secondly, the original YOLOv5 model was improved by replacing the Bottleneck network in the C3 module with the lightweight GhostNet V2 to reduce the network parameters, and changing the box loss function CIoU to SIoU in the loss function to make the regression of the detection box more accurate, and coordinate attention mechanism (CA) was added to the network to reduce the interference of useless background information in images. Before training, pre-anchor boxes were generated by using IoU-based K-means clustering, after that the dataset was fed into the improved YOLOv5 for training, and the trained model was used to detect the trunks. Finally, weighted boxes fusion (WBF) was used instead of the non-maximum suppression (NMS) in this paper for the output of the detection boxes. Then the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used for trunk clustering. The improved target detection method was trained and validated on the experimental dataset. The model size is reduced by 43.6 %, model parameters are reduced by 46.9 %, and the mAP reaches 97.1 %, with an average detection speed of 198.2 ms per image. Compared with the original YOLOv5, the model is more lightweight, the detection accuracy and speed are improved. The improved YOLOv5 is also better than YOLOv3, NanoDet and SSD in terms of combined accuracy and speed, and has similar performance to YOLO_MobileNet in orchard dataset. The experimental results show that the improved YOLOv5 target detection model proposed in this paper is lightweight while still having better detection accuracy and detection speed in complex environments, and the model is small enough to be deployed to mobile or low-performance terminals for target detection in natural orchard environments. [ABSTRACT FROM AUTHOR]

• An anti-lodging machine, comprising an anti-lodging device and an AGV (Automated Guided Vehicle) was designed to replace manual methods. • A machine vision system was developed to aim the anti-lodging device at the crown buds. • The YOLOv5s algorithm is enhanced for detecting immature pineapple crown buds. • The mAP_0.5 of the improved YOLOv5s algorithm is 3.71 %, 1.69 %, and 1.19 % higher than the original YOLOv5s, SSD, and Faster R-CNN, respectively. • The operating speed of this intelligent anti-lodging method is 2.7 times faster than manual binding. Sunscorch, a common disease in pineapple cultivation, is caused by direct sunlight on the surface of lodging pineapple fruits. To mitigate this issue, pineapple fruits are often bagged or supported with fixing rods. This paper presents the design of an anti-lodging machine, comprising an anti-lodging device and an AGV (Automated Guided Vehicle), and develops a machine vision system to precisely aim the anti-lodging device at the crown buds. The YOLOv5s algorithm is enhanced for detecting crown buds. The improvement of the YOLOv5s algorithm includes the introduction of the attention mechanism CBAM to enhance feature extraction, the addition of BiFPN and GhostNet to strengthen feature fusion and to reduce detection time and computational cost. By transforming pixel coordinates, the relative coordinates between the crown bud and the device are calculated to achieve accurate targeting. The experiments demonstrate that the precision, recall, and map_0.5 of the improved YOLOv5s algorithm are 91.89 %, 92.25 %, 94.01 %, and the detection speed is 15.71 ms/image. The average detection speed is 1.14 ms/image and 9.45 ms/image faster than that of SSD and Faster R-CNN, benefiting from a parameter number only 3/4 and 3/7 of SSD and Faster R-CNN, respectively. The results of contrast experiments indicate that, although the lodging prevention rate of this intelligent anti-lodging method is 4 % lower than the manual binding, its operating speed is 2.7 times faster. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep learning automates precise cereal plant head detection in agriculture. • Maize, rice, wheat, and sorghum case studies evaluated. • Comparison of deep learning approaches for object detection and image segmentation. • Challenges and opportunities for future research outlined. • Emphasis on how deep learning can contribute to increasing crop yields and food security. Cereal plant heads must be identified precisely and effectively in a range of agricultural applications, including yield estimation, disease detection, and breeding. Traditional methods that rely on manual feature extraction and thresholding take a lot of time and work, and they are also impacted by crop variability. Deep learning algorithms can be used to automate this procedure because they can directly extract complicated information from images and produce cutting-edge outcomes. This review provides a comprehensive overview of recent research on deep learning-based head detection in cereal plants, emphasizing object detection and image segmentation. We also discuss the major benefits and drawbacks of different deep learning architectures and training methods, as well as examples of their application in maize, rice, wheat, and sorghum. Developing robust image processing algorithms, using deep learning in other domains like unmanned aerial vehicles, and utilizing large and diverse datasets are all challenges outlined in our study as future research directions. Through the integration of advanced computer vision techniques with precision agriculture, this paper attempts to promote further research and innovation in this intriguing field. We provide a thorough analysis of current developments in deep learning-based head detection for cereal plants and emphasize how this technology can contribute significantly to precision agriculture. [ABSTRACT FROM AUTHOR]

• A new deep learning framework to detect dairy cow lameness was proposed. • This method was more stable in the antagonism of environmental robustness. • This method improves the detection accuracy of dairy cow lameness. Manual detection of lameness poses several problems, such as difficulty in finding sudden, severe or early lameness behavior. A dairy cow's lameness is closely related to curvature of the cow's back. Focusing on the curvature features of dairy cows' backs, this study proposes a lameness detection method that combines machine vision technology with a deep learning algorithm. Firstly, the FLYOLOv3 algorithm was used to construct a Cow's Back Position Extraction (CBPE) model to realize the extraction of the dairy cow's back position coordinates. Simultaneously, a First to Last Frame Image Difference (FLFID) algorithm was used to construct a Cow's Object Region Extraction (CORE) model to separate the dairy cow from the image background and obtain the pixel region of the dairy cow. Then, a Cow's Back Curvature Extraction (CBCE) model was used to extract the dairy cow's back curvature data from the acquired dairy cow's back position and pixel region of the dairy cow. Finally, a Noise+ Bilateral Long Short-term Memory (BiLSTM) model was used to predict the curvature data and match the curvature features of the dairy cow's lameness, so as to classify and detect dairy cow lameness. To verify the effectiveness of the algorithm, 567 videos were used to train the network model in a Long Short-term Memory (LSTM) model, a BiLSTM model, Noise+LSTM model, and the model proposed in this paper, respectively, and 243 videos were used for verification and testing. According to the fitting curvature data of the dairy cows' back obtained by the algorithm used in this paper, it was found that the average classification accuracy of the model proposed in this research was 8.04%, 2.09%, and 5.78% higher than the average classification accuracy of the LSTM, BiLSTM, Noise+LSTM models, respectively. In the parallel experiment that classified the detection of dairy cow lameness, the average classification accuracy of the model proposed in this paper was 96.61%. The above results show that the lameness of dairy cows can be correctly detected through analysis of the curvature features of dairy cows' backs. The proposed method is a novel, deep learning-based method for dairy cow lameness early detection which may have significant economic impact on the dairy industry, and the proposed method provides an innovative means for detecting dairy cow lameness. [ABSTRACT FROM AUTHOR]

• The software can extract 54 phenotypic features of various-sized plant seeds. • These features provide efficient separation of seeds with different qualities. • Using features for modeling can realize seed clarity, vigor, and purity testing. • The established models can be applied in AIseed for visual seed quality testing. Seed quality is the key to seedling development, which affects field yield. Researchers, breeders, and processors may need to investigate seed quality on a large scale, which requires high-throughput methods, which are currently lacking. To address this bottleneck, we have developed a novel software named AIseed that automatically captures and analyzes great traits, such as the shape, color, or texture features of individual seeds, which are rarely effortlessly and stably determined by conventional measurements. The AIseed software is based on machine vision technology and enables high-throughput handling and phenotyping of various-sized plant seeds. After phenotyping for a total of 54 features, AIseed numbers each seed to facilitate further seed quality assessment or grain detection. In addition, AIseed includes modules for seed quality detection and prediction, such as seed clarity, purity, vigor, and viability testing, which are based on machine learning or deep learning models developed by analyzing the associations between seed quality indicators and the features obtained by the software. Through a series of experiments, this paper has confirmed that AIseed has a high performance in extracting phenotypic features and testing seed quality from images for seeds of several plants of different sizes, with high speed and high precision in separating seeds from the background and then analyzing them. In summary, it is a non-destructive and highly effective software for accurate seed phenotyping and seed quality assessment, which is essential for both breeding and yield improvement. [ABSTRACT FROM AUTHOR]

[Display omitted] • Plant stand count methods using unmanned aerial vehicle imagery were reviewed. • Computer vision methods produced promising results in plant stand count analysis. • Several research gaps were identified and recommendations provided. • Results will be helpful to stakeholders like farmers, producers, and researchers. Plant stand count helps in estimating the yield and evaluating the planter's efficiency and seed quality. Traditional methods of counting the plants by manual measurement are time-consuming, laborious, and error-prone. In contrast, the ground-based sensing methods are limited to smaller spaces. High spatial resolution images obtained from unmanned aerial vehicles (UAV) can be used in conjunction with computer vision algorithms to evaluate plant stand count, as it directly influences the yield. In spite of the importance of high-throughput plant stand count in row crop agriculture, no synthesized information in this specific subject matter is available. Therefore, the objective of this paper was to perform a systematic literature review of the current studies that focus on evaluating plant stand count using UAV imagery to provide well-synthesized information, identify research gaps, and provide suitable recommendations. In this study, a comprehensive literature search was performed on three academic databases (Agricola, Web of Science, and Scopus), and a total of 29 articles were found based on search terms and selection criteria for review. From the systematic review, it can be concluded that: an appropriate stage after plant emergence without canopy overlap is necessary for image acquisition; optimal flying height should be selected to balance the field coverage and accuracy; L*a*b* color space can provide better segmentation; hyperspectral camera imagery can provide good discrimination; deep learning with data augmentation and transfer learning models can be used to reduce the computational time and resources; the stand count methodology that is successful with corn and cotton could be extended to other row crops and horticultural crops; and application of direct image processing and use of open-source platforms is required for stakeholder participation. The review will be helpful to the farmers, producers, and researchers in selecting and employing the UAV-based algorithms for evaluating plant stand count. [ABSTRACT FROM AUTHOR]