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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]

[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]

• An overview of generative adversarial network (GAN) architectures is presented. • The application of GANs in agriculture is reviewed. • The challenges and opportunities of GANs are discussed. In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and ground-truthed image datasets are tremendously beneficial but most often difficult to obtain to fuel the development of highly performant models. As artificial intelligence through deep learning is impacting analysis and modeling of agricultural images, image augmentation plays a crucial role in boosting model performance while reducing manual efforts for image collection and labelling, by algorithmically creating and expanding datasets. Beyond traditional data augmentation techniques, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representations and generate highly realistic samples. Since 2017, there has been a growth of research into GANs for image augmentation or synthesis in agriculture for improved model performance. This paper presents an overview of the evolution of GAN architectures followed by a first systematic review of various applications in agriculture and food systems (https://github.com/Derekabc/GANs-Agriculture), involving a diversity of visual recognition tasks for plant health conditions, weeds, fruits (preharvest), aquaculture, animal farming, plant phenotyping as well as postharvest detection of fruit defects. Challenges and opportunities of GANs are discussed for future research. [ABSTRACT FROM AUTHOR]