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Crop counting is a crucial step in crop yield estimation. By counting, crop growth status can be accurately detected and adjusted, improving crop yield and quality. In recent years, with the rapid development of convolutional neural networks, deep learning-based object detection methods have been widely used in crop counting. By summarizing the research related to crop counting, this paper reviews the development status of object detection and crop counting. It then compares deep learning-based object detection counting methods with other counting methods. The paper also introduces public datasets and evaluation metrics commonly used for algorithmic models and provides a more in-depth analysis of the application of object detection in crop counting. Finally, the current problems that need to be solved, such as the lack of datasets, difficulties in small object counting, occlusion in complex environments, and some future directions are summarized. We hope this review will encourage more researchers to use deep-learning object detection methods in agriculture. • The paper compares the advantages and disadvantages of the methods currently used for crop counting tasks. • It is the first summary of object detection-based methods' application in crop counting tasks, covering commonly used models, issues, and challenges. • Public datasets utilized for crop counting tasks through object detection-based methods have been summarized. [ABSTRACT FROM AUTHOR]

• Complete and accurate counting of fruit on the tree. • High performance single stage deep Convolution Neural Network YOLOX used. • Superior detection rate (≥99% when R ≤ 1.2 m) was obtained. • Excellent robustness in complex orchard scenarios. Fruits counting is important in management of orchard and plantation since better decision for labor and logistic can be made based on complete and accurate counting of fruits. Computer vision-based fruits counting has been research focus as it's an automatic way for recognition of dense fruit on the branch. However, complete fruits counting of a whole tree hasn't hardly been studied. And there is a lack of robust and accurate fruits counting method in complex orchard scenarios, like covering, shadow, clustering in image. In this paper, a panoramic method for fruit complete yield counting based on deep learning object detection is proposed, and was validated on a holly tree with dense fruits. Firstly, images were taken surrounding the fruit trees using UAV, and SIFT based images matching were performed to form a complete panoramic unfolding map of the fruit tree surface. Then, a YOLOX object detection network was built and trained with novel samples augmentation and composition strategies. Finally, fruits counting YOLOX was performed on the panorama to count the whole plant fruits number. The accuracy and effectiveness of this method were tested at different scales and scenarios. The results show that: (1) high-quality panoramic images can be built for an accurate fruit number counting. (2) The Statistical Rate (SR) between detected number and actual number is as high as SR > 96% when the ring shot parameter of Holly tree is R ≤ 1.2 m, SR > 95% when R ≤ 1.6 m. The Detection Rate between detected number and captured number in the panorama image is over 99% when R ≤ 1.2 m and over 97% when R ≤ 2.0 m. The result is superior to previous researches. (3) it has good robustness against shading, covering, incomplete contour. Comparisons between the proposed method and other methods has been done and the result show the proposed method is the most effective in fruits counting. Moreover, we proposed and verified the positive effects of Gaussian convolution kernel and γ-component control on fruit detection rate. The YOLOX-based fruit counting method can be extended to a wide range of fruits, like apples, lychee and so. Moreover, YOLOX has excellent inferencing efficiency which makes it a good potential for real-time application in orchard and plantation management. [ABSTRACT FROM AUTHOR]

• Automatic date palm tree detection algorithm in drone imagery. • Detecting crowded date palm trees using YOLO-V5. • Comparison of one-stage object detection methods for date palm tree detection. Date palm trees are an important economic crop in the Arabian Peninsula, Middle East, and North Africa. Counting the numbers and determining the locations of date palm trees are important for predicting the date production and plantation management. In this paper, we exploit the effective use of the state-of-the-art CNN, YOLO-V5, in detecting date palm trees in images captured by a camera onboard of a drone flying 122 m above farmlands in the Northern Emirates of the United Arab Emirates (UAE). In the dataset preparation process, we randomly selected 125 captured images and divided them into three datasets: training (60%), validation (20%), and testing (20%). The images of date palm trees in the training and validation datasets were manually annotated and those in the training dataset were used to train the four sub-versions of YOLO-V5 CNNs. The validation dataset was used during the training process to assess how well the network was performing during training. Finally, the images in the test dataset were used to evaluate the performance of the trained models. The results of using YOLO-V5 for date palm tree detection in drone imagery are compared with those obtainable with other popular CNN architectures, YOLO-V3, YOLO-V4, and SSD300, both quantitatively and qualitatively. The results show that for the amount of training data used, YOLO-V5m (medium depth) model records the highest accuracy, resulting in a mean average precision of 92.34%. Further it provides the ability to detect and localize date palm trees of different sizes, in crowded, overlapped environments and areas where the date palm tree distribution is sparse. Therefore, it is concluded that the method can be a useful component of an automated plantation management system and help forecast the quantities of date production and condition monitoring of the date palm trees. [ABSTRACT FROM AUTHOR]

• An image synthesis and semi-supervised learning pipeline is proposed. • The pipeline constructs an image library of plant instances with pixelwise masks. • Images can be synthesized using the library for site-specific weed detection. • Without manual labels, the pipeline achieves precision close to supervised learning. • Ablation study provides insights into the construction of a weed image database. Precise and efficient weed detection in agricultural fields is the key for robotic weed control. Recent developments in convolutional neural networks (CNNs) have achieved significant success in this regard. CNNs that simultaneously localize and classify objects in images are the predominant forms that have been widely used to detect crops and weeds. However, the use of CNNs in agriculture, particularly for weed detection, has been impeded by a lack of large training dataset with ground truth annotations. Cut-and-paste image synthesis approach and semi-supervised learning are popular methods to alleviate the training data deficiency problem. In this paper, we propose a novel image synthesis and semi-supervised learning pipeline to train site-specific weed detection models without the need for manually labeled images. The CNN models trained by this pipeline achieve performance levels close to that of the supervised models. We investigated the behavior of the proposed pipeline by varying several key components and showed that color match between the training and testing images, training-time color augmentation, and iterative semi-supervised learning largely improve the model performance. These promising results can be used to guide the construction of a weed image database applicable to different weed detection scenarios. [ABSTRACT FROM AUTHOR]

• Graph based method to detect and remove duplicated cattle using multiple UAV's images. • Competitive results with significant reduction of runtime. • Novel benchmark image collection for cattle detection and counting in large areas. Among the production areas with largest impact on global economy, agriculture and livestock play a prominent role. Technologies have been developed in order to automate and increase the efficiency of these fields. The use of Unmanned Aerial Vehicles (UAVs) has been extensively investigated to improve the efficiency of agricultural production and in the monitoring of animals. One of the most important and challenging tasks in animal monitoring is cattle counting. In this paper, we propose a method for detecting and counting cattle in aerial images obtained by UAVs, based on Convolutional Neural Networks (CNNs) and a graph-based optimization to remove duplicated animals detected in overlapped images. We show that maximizing the degree of matching between animals is a suitable strategy to reduce duplicate counting. We also offer a dataset of real images, obtained from large pasture areas, both for training as well as for testing/benchmarking of cattle counting techniques. Our results show that the proposed method is very competitive, outperforming the state of the art in detecting duplicated animals, while significantly reducing the computational cost of the overall counting task. [ABSTRACT FROM AUTHOR]