Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination of remote sensing technology and machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive and systematic reviews on the integrated application of these two technologies. For this reason, this study conducts a systematic literature search using the Web of Science, Scopus, Google Scholar, and PubMed databases and analyzes the integrated application of remote sensing technology and machine learning algorithms in PA over the last 10 years. The study found that: (1) because of their varied characteristics, different types of remote sensing data exhibit significant differences in meeting the needs of PA, in which hyperspectral remote sensing is the most widely used method, accounting for more than 30% of the results. The application of UAV remote sensing offers the greatest potential, accounting for about 24% of data, and showing an upward trend. (2) Machine learning algorithms displays obvious advantages in promoting the development of PA, in which the support vector machine algorithm is the most widely used method, accounting for more than 20%, followed by random forest algorithm, accounting for about 18% of the methods used. In addition, this study also discusses the main challenges faced currently, such as the difficult problems regarding the acquisition and processing of high-quality remote sensing data, model interpretation, and generalization ability, and considers future development trends, such as promoting agricultural intelligence and automation, strengthening international cooperation and sharing, and the sustainable transformation of achievements. In summary, this study can provide new ideas and references for remote sensing combined with machine learning to promote the development of PA.

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model.

In modern agriculture, the proliferation of weeds in cotton fields poses a significant threat to the healthy growth and yield of crops. Therefore, efficient detection and control of cotton field weeds are of paramount importance. In recent years, deep learning models have shown great potential in the detection of cotton field weeds, achieving high-precision weed recognition. However, existing deep learning models, despite their high accuracy, often have complex computations and high resource consumption, making them difficult to apply in practical scenarios. To address this issue, developing efficient and lightweight detection methods for weed recognition in cotton fields is crucial for effective weed control. This study proposes the YOLO-Weed Nano algorithm based on the improved YOLOv8n model. First, the Depthwise Separable Convolution (DSC) structure is used to improve the HGNetV2 network, creating the DS_HGNetV2 network to replace the backbone of the YOLOv8n model. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the feature fusion layer, further optimizing the model’s ability to recognize weed features in complex backgrounds. Finally, a lightweight detection head, LiteDetect, suitable for the BiFPN structure, is designed to streamline the model structure and reduce computational load. Experimental results show that compared to the original YOLOv8n model, YOLO-Weed Nano improves mAP by 1%, while reducing the number of parameters, computation, and weights by 63.8%, 42%, and 60.7%, respectively.

Efficient weed detection in dryland spring wheat fields is crucial for sustainable agriculture, as it enables targeted interventions that reduce herbicide use, minimize environmental impact, and optimize resource allocation in water-limited farming systems. This paper presents HSG-Net, a novel lightweight object detection model based on YOLOv8 for weed identification in dryland spring wheat fields. The proposed architecture integrates three key innovations: an HGNetv2 backbone for efficient feature extraction, C2f-S modules with star-shaped attention mechanisms for enhanced feature representation, and Group Head detection heads for parameter-efficient prediction. Experiments on a dataset of eight common weed species in dryland spring wheat fields show that HSG-Net improves detection accuracy while cutting computational costs, outperforming modern deep learning approaches. The model effectively addresses the unique challenges of weed detection in dryland agriculture, including visual similarity between crops and weeds, variable illumination conditions, and complex backgrounds. Ablation studies confirm the complementary contributions of each architectural component, with the full HSG-Net model achieving an optimal balance between accuracy and resource efficiency. The lightweight nature of HSG-Net makes it particularly suitable for deployment on resource-constrained devices used in precision agriculture, enabling real-time weed detection and targeted intervention in field conditions. This work represents an important advancement in developing practical deep learning solutions for sustainable weed management in dryland farming systems.