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• Precision agriculture method applicable to remote on-tree pomegranate pixel-based fruit size monitoring. • Comparison of Deep Learning and zero-shot vision Transformer instance segmentation models. • Image-Based size estimation algorithm for occluded fruits with protuberances. In situ automatic fruit monitoring is of great interest for more accurate and cost-efficient decision making in agriculture. For this purpose, the development of computer vision-based tools is essential. Deep Learning techniques have shown good performance in fruit detection and segmentation. Recently, new models based on Transformer architecture have emerged with promising potential and zero-shot inference capability. In this paper, a Deep Learning model, Mask R-CNN, was trained for on-tree pomegranate fruit segmentation and compared with foundational models based on Vision Transformer, Grounding DINO and Segment Anything Model. Results with Mask R-CNN proved a better performance, according to F1 score and AP metrics, and a lower computational cost, according to prediction time. One of the most interesting derived applications from fruit segmentation is fruit size estimation. However, segmented fruit masks are frequently incomplete due to occlusions. Therefore, image fruit size estimation is not a straightforward process. In this work, we also propose a novel algorithm to estimate and monitor the fruit size in pixel units from the automated masks. A median relative error of 1.39% was obtained, demonstrating the potential and feasibility of future fully-automatic fruit size estimators. [ABSTRACT FROM AUTHOR]

• Detailed review of over 300 research papers. • Object Detection and Tracking are suitable techniques to enhance Precision Farming. • Critical discussion regarding the challenges of Object Detection and Tracking. • Potential research lines to improve Object Detection and Tracking are identified. Object Detection and Tracking have gained importance in recent years because of the great advances in image and video analysis techniques and the accurate results these technologies are producing. Moreover, they have successfully been applied to multiple fields, including the agricultural domain since they offer real-time monitoring of the status of the crops and animals while counting how many are present within a field/barn. This review aims to review the current literature on Object Detection and Tracking within the field of Precision Farming. For that, over 300 research articles were explored, from which 150 articles from the last five years were systematically reviewed and analysed regarding the algorithms they implemented, the domain they belong to, the difficulties they faced, and which limitations should be tackled in the future. Lastly, it examines potential issues that this approach might have, for instance, the lack of open-source datasets with labelled data. The findings of this study indicate that Object Detection and Tracking are critical techniques to enhance Precision Farming and pave the way for robotization for the agricultural sector since they provide accurate results and insights on crop and animal management, and optimize resource allocation. Future work should focus on the optimal acquisition of the datasets prior to Object Detection and Tracking, along with the consideration of the biophysical environment of the farming scenarios. [ABSTRACT FROM AUTHOR]

The detection of pests plays a crucial role in intelligent early warning systems of injurious insects and diseases in precision agriculture. However, pests strong concealment and mobility pose significant challenges to their timely detection. In this paper, we propose a novel approach called Multi-scale Dense YOLO (MD-YOLO) for detecting three typical small target lepidopteran pests on sticky insect boards. In MD-YOLO, we design three key components: the image feature extraction part, the feature fusion network, and the prediction module. To enhance the utilization of feature maps and mitigate information loss, we incorporate DenseNet blocks and an adaptive attention module (AAM) into the feature extraction part. The AAM helps capture relevant image details and improves the model's ability to exploit feature representations effectively. For effective feature integration, our feature fusion network incorporates both a feature extraction path and a feature aggregation path. This enables the deep network to leverage spatial location information from the shallower network, thereby enhancing the detection accuracy. Experimental results demonstrate the effectiveness of MD-YOLO, with detection results achieving an mAP@.5 value of 86.2%, an F1 score of 79.1%, and an IoU value of 88.1%. We conduct extensive experiments to compare MD-YOLO with state-of-the-art models, and the results showcase its superiority. Furthermore, we design an Internet of Things (IoT) system that demonstrates MD-YOLO's performance in real-world field scenes, highlighting its practical applicability. • Proposing an MD-YOLO network for the detection of 3 different species of small target pests. • Proposing a random splicing method and a random sprinkling method for image data augmentation. • Accomplishing the deployment of MD-YOLO in a pest early warning software. [ABSTRACT FROM AUTHOR]

• A survey of machine vision for fish tracking in aquaculture. • A taxonomy of the techniques and classify the papers based on the taxonomy. • An analysis of fish detection and tracking methods. • A review of the application of fish tracking technology in fish research. • Summarized the datasets that can be used for fish tracking. In aquaculture, Behavioral monitoring of fish contributes to scientific management and reduces the threat of loss from disease and stress. Fish tracking technology plays an important role in behavior monitoring. It can pay attention to the movement of fish at any time and discover various abnormal behaviors in time. As a non-invasive method, computer vision is a powerful tool for fish tracking.. Its tracking principle is to establish the relationship between fish positions in a continuous video sequence and get the complete movement trajectory of the fish. Nevertheless, computer vision modeling used for fish tracking is riddled with many challenges, such as fish deformation, frequent occlusion, scale change, etc. Around these difficult issues, many scholars have carried out the research. In this paper, we review the progress of tracking algorithms in fish research. Then, methods for fish tracking before deep learning are introduced. Further, a detailed discussion of fish tracking methods employing deep learning such as tracking-by-detection, deep features combined with correlation filtering methods, Siamese networks, etc. Furthermore, we summarize datasets that can be used as fish tracking and give evaluation metrics in target tracking algorithms. In addition, experimental data of several mainstream tracking algorithms on a public tracking dataset are given. Finally, we discuss the outstanding findings and look forward to the fish tracking method combined with Transformer, aiming to provide a reference for accelerating the promotion of smart fishery and precision farming. [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]

Image and video tools for analyzing crop scenes and plants are essential for applying precision agriculture to crop maintenance, harvesting, or pruning. In this paper, we are interested in vine pruning, a task that requires a precise understanding of the vine structure with branch type identification, orientations, and node locations. However, estimating such a structure is highly challenging, given the large variety in grapevine appearances, lighting conditions, viewpoint, the interweaving of branches, occlusions, and the level of details needed. To address these challenges, we propose ViNet: a deep-learning approach for estimating the structure of grapevine, which comprises two main steps: The first one detects nodes and identifies the branch types of the plant, as well as the spatial relation between them, whilst the second one uses the extracted nodes and branches to build a graph, out of which the structure of the grapevine is inferred. In doing so, we make four main contributions: (i) we put forward for the first time a method for automatic segmentation and extraction of the grapevine structure from images; (ii) we propose a novel approach leveraging the powerful stacked hourglass network to infer node location, branch types and the spatial relationships between them; (iii) we propose a novel shortest path weighted graph optimization step to extract connections between nodes and infer the structure, allowing to address the problem of having an unknown number of branches in the tree; (iv) we publicly release a dataset of more than 1500 grapevine images fully annotated with the structure information. Extensive experiments on this dataset demonstrate the efficiency of our approach at predicting the structure of a grapevine, achieving a precision and recall for node prediction of 95% and 90%, respectively, as well as ablation studies validating our design choices. • Deep learning-based computer vision for viticulture • Grapevine nodes, branches, and spatial relationships are estimated from a CNN • Plant structure is recovered using a graph and Dijkstra algorithm • A grapevine single Guyot dataset is publicly released [ABSTRACT FROM AUTHOR]