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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]

AgroCounters is an open-source repository for counting objects in images in the agricultural domain by utilizing deep-learning algorithms. In this paper, we present the framework of AgroCounters, which integrates state-of-the-art deep learning models, including regression-based counting, detection-based counting, and density-estimation-based counting, to accurately count various agricultural objects, such as fruits, vegetables, and livestock, in single images. The framework utilizes transfer learning techniques to optimize model performance on the limited labeled data available in the agricultural domain. We provide an open-source implementation of AgroCounters, which includes a multitude of algorithms for counting applications and a toolbox that includes metrics, training data tools, visualizations, and a simple installation guide for several open-source implementations of counting methods. We evaluated the performance of AgroCounters on multiple agricultural datasets acquired from RGB sensors, including plant leaves, melons, wheat grains, cherry tomatoes, grapes, apple flowers, bananas (fruit and leaves), pears, and chickens. We compared the results of the various implemented methods over these datasets and showcased the most suitable solution for each. YOLOv5, the most recent of the compared object detectors, provided the best results on all the examined datasets, and there was no clear 'winner' between Faster-RCNN and RetinaNet. Based on the analyzed datasets, when higher accuracy is required, the direct regression network (DRN) should be used; for small datasets, multiple scale regression (MSR) gives superior results. Based on the developments, we proposed guidelines for developing deep-learning-based counting solutions for agricultural applications, focusing on solutions and best practices for the agricultural domain. Overall, AgroCounters presents a promising solution for automated counting in the agricultural domain, offering significant potential for reducing manual labor, improving crop management, and increasing productivity. • AgroCounters is a repository for counting agriculture objects using deep learning. • AgroCounters includes regression, detection, and density estimation algorithms. • AgroCounters provides toolboxes for metric calculation, training, and visualization. • AgroCounters publicizes 10 different datasets with annotations and trained models. • Present guidelines for developing deep learning-based counters in agriculture. [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]

• A dataset on small target pests on greenhouse sticky traps. • A novel solution was developed for counting multiple small targets based on density maps, simultaneously. • An adaptive sample equalization algorithm was developed to tackle the issue of class imbalance. Whiteflies and fruit flies are common pests that adversely affect greenhouse crops, so it is vital to control their numbers promptly. Current research involves setting up artificial monitoring devices with fixed yellow sticky traps (YSTs), for pest surveillance. However, these devices can only be installed in specific environments and additional interventions may impact the actual distribution of greenhouse pests. On the other hand, detection methods, which are the most commonly used, struggle to accurately detect overlapping or closely interacting small insects, which is a common occurrence in the presence of pests. In this paper, we propose a novel solution for the multi-species pest recognition and counting method based on a density map to tackle pest counting in the greenhouse. To reduce interference between different types of objects, we used two annotation maps to represent the distribution of each object separately. Then, two counting branches are designed to count the pests, respectively. Finally, the detected pests are mapped onto the original image to display their spatial positions. In addition, an adaptive sample equalization method is proposed to address the issue of class imbalance in images, and the atrous spatial pyramid pooling (ASPP) structure and the CBAM attention mechanism are added to the fruit fly branches to focus on learning fruit fly features from crowded edges of YSTs. When applied to split images, our method achieved reliable counting performance in counting both whiteflies and fruit flies, with R2 scores of 0.973 and 0.972, respectively. This method has been embedded into mobile devices to assist greenhouse workers in monitoring pest populations in the field. The experiments show that the proposed method provides a promising direction for counting multiple small target insects and monitoring pest populations in crowded images. At the same time, it demonstrates the potential of density map methods in various small target detection scenarios. [ABSTRACT FROM AUTHOR]

[Display omitted] • We developed AICropCAM, an edge-computing enabled camera system for crop monitoring. • It integrated Raspberry Pi and Arduino for image processing and LoRa communication. • It ran a stack of four deep neural networks to characterize multiple plant/canopy parameters. • We quantified the power consumption and speed of the system for edge image-processing. • AICropCAM is a next-generation enabling technology for real-time, low-latency Ag applications. Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with >43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations. [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]

[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]

• Mechanical weeding module for precise hoeing in vegetable farming. • Real-time precise stem detection in RGB images on embedded computer. • Deep learning object detection based neural network for precise stem location. • Tracking algorithm for temporal aggregation of stems in successive frames. • F1-score of 94.74% and 93.82% for maize and bean with sub-centimetric location accuracy. Developing alternatives to the chemical weeding process usually carried out in vegetable crop farming is necessary in order to reach a more sustainable agriculture. However, a precise mechanical weeding requires specific sensors and advanced computer vision algorithms to process crop and weed discrimination in real-time. In this paper we propose an algorithm able to detect, locate, and track the stem position of crops in images which is suitable for precision actions in vegetable fields such as mechanical hoeing within crop rows. The algorithm is twofold: (i) a deep neural network for object detection is first used to detect crop stems in individual RGB images and then (ii) an aggregation algorithm further refines the detections taking advantage of the temporal redundancy in consecutive frames. We evaluated the pipeline on images of maize and bean crops at an early stage of development, acquired in field conditions with a camera embedded in an experimental mechanical weeding system. We reported F1-scores of respectively 94.74% and 93.82% with a location accuracy around 0.7 cm when compared with human annotation. Moreover, this pipeline can operate in real-time on an embedded computer consuming as little power as 30 W. [ABSTRACT FROM AUTHOR]