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[Purpose/Significance] The Ministry of Agriculture and Rural Affairs of China and the agricultural administrative departments of various provinces play a major role in promoting agricultural and rural big data development. This paper aims to learn, analyze and evaluate the data publishing, sharing, standardization and applications by investigating the open governmental agricultural data in 31 provinces, autonomous regions and municipalities in the mainland of China, and propose suggestions for improvement accordingly. [Method/Process] We investigated the open government platforms built by 31 provinces (autonomous regions and municipalities), sorted out the content published in these platforms, and standardized and summarized the investigation data. We made an analysis based on the update time of data, license of data applications, and download of data. Meanwhile, combined with FAIR data principles, we evaluated the data from the findability, accessibility, interoperability and reuse of the data.[Results/Conclusions] At present, the management, publishing and sharing of agricultural data in China is at an elementary stage. Although various governments formulated regulations and policies, the implementation of these regulations and policies is unsatisfactory. The Ministry of Agriculture and Rural Affairs of China should join hands with the agricultural administrative departments of various provinces to promote the opening and use of government agricultural data. Meanwhile, they should be in line with the construction mode of the international open government data and improve their capabilities in data publishing, management and services.

The demand for intelligent agriculture is increasing due to the continuous impact of world food and environmental crises. Focusing on fruit detection, with the rapid development of object detection technology, it is now possible to achieve high efficiency and high accuracy in fruit detection systems. However, detecting fruit with high precision in unstructured orchard environments remains particularly challenging. Such environments, which are composed of varying illumination conditions and degrees of occlusion, can be mitigated by certain strategies. To our knowledge, this is the first time that optimization strategies used in fruit detection have been reviewed. This review aims to explore methods for improving fruit detection in complex environments. First, we describe the common types of complex backgrounds found in outdoor orchard environments. Subsequently, we divide the improvement measures into two categories: optimization before and after image sampling. Next, we compare the test results obtained before and after the application of these improved methods. Finally, we describe the future development trends of fruit detection optimization technology in complex backgrounds. We hope that this review will inspire researchers to design their optimization strategies and help explore lower-cost and more robust fruit detection systems.

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.

Color-changing melon is an ornamental and edible fruit. Aiming at the problems of slow detection speed and high deployment cost for Color-changing melon in intelligent agriculture equipment, this study proposes a lightweight detection model YOLOv8-CML.Firstly, a lightweight Faster-Block is introduced to reduce the number of memory accesses while reducing redundant computation, and a lighter C2f structure is obtained. Then, the lightweight C2f module fusing EMA module is constructed in Backbone to collect multi-scale spatial information more efficiently and reduce the interference of complex background on the recognition effect. Next, the idea of shared parameters is utilized to redesign the detection head to simplify the model further. Finally, the α-IoU loss function is adopted better to measure the overlap between the predicted and real frames using the α hyperparameter, improving the recognition accuracy. The experimental results show that compared to the YOLOv8n model, the parametric and computational ratios of the improved YOLOv8-CML model decreased by 42.9% and 51.8%, respectively. In addition, the model size is only 3.7 MB, and the inference speed is improved by 6.9%, while mAP@0.5, accuracy, and FPS are also improved. Our proposed model provides a vital reference for deploying Color-changing melon picking robots.

Agriculture is a vital sector in the global economy but also a major contributor to water consumption and wastage due to inefficient irrigation techniques. To address this issue, smart irrigation systems using the Internet of Things (IoT) have emerged as a solution for optimizing water use. These systems utilize real-time sensor data to improve irrigation efficiency and agricultural productivity. This paper presents an automatic, low-cost intelligent irrigation system based on a fuzzy rule-based inference approach and an energy-aware routing algorithm. The proposed system determines the optimal irrigation method using sensor data and ensures efficient information transmission through a fast fuzzy-based routing mechanism. Additionally, it enables remote monitoring and control via mobile devices, enhancing user convenience. The system is designed to be intelligent, cost-effective, and portable, making it suitable for various agricultural applications such as greenhouses and farms. Simulation results demonstrate that the proposed method outperforms existing algorithms, including DLQR, SPIS, and FWIS, in terms of network lifetime and power consumption. By improving water efficiency and reducing resource wastage, this research contributes to sustainable agriculture. The proposed system provides a scalable and adaptable solution for modern farming, ensuring better irrigation management while conserving essential resources.

Smart rice disease detection is a key part of intelligent agriculture. To address issues like low efficiency, poor accuracy, and high costs in traditional methods, this paper introduces an enhanced lightweight version of the YOLOv11-RD algorithm, enhancing multi-scale feature extraction through the integration of the enhanced LSKAC attention mechanism and the SPPF module. It also lowers computational complexity and enhances local feature capture through the C3k2-CFCGLU block. The C3k2-CSCBAM block in the neck region reduces the training overhead and boosts target learning in complex backgrounds. Additionally, a lightweight 320 × 320 LSDECD detection head improves small-object detection. Experiments on a rice disease dataset extracted from agricultural operation videos demonstrate that, compared to YOLOv11n, the algorithm improves mAP50 and mAP50-95 by 2.7% and 11.5%, respectively, while reducing the model parameters by 4.58 M and the computational load by 1.1 G. The algorithm offers significant advantages in lightweight design and real-time performance, outperforming other classical object detection algorithms and providing an optimal solution for real-time field diagnosis.

With the rapid growth of the Internet of Things, intelligent agriculture is becoming increasingly important. Traditional agricultural monitoring methods, which rely on fossil fuels and complex wiring, hinder progress. This work introduces a hybrid nanogenerator based on a rotational-swinging mechanism (RSM-HNG) that combines triboelectric nanogenerators (TENGs) and electromagnetic generators (EMGs) for efficient wind energy harvesting and smart agriculture monitoring. The parallelogram mechanism and motion conversion structure enable the stacking and simultaneous contact-separation of multiple TENG layers. Moreover, it allows the TENG and EMG units to operate simultaneously, which improves energy harvesting efficiency and extends the system’s lifespan compared to traditional disc-based friction wind energy harvesting methods. With four stacked layers, the short-circuit current of the TENG increases from 16 μA to 40 μA, while the transferred charge rises from 0.3 μC to 1.5 μC. By optimizing the crank angle, material selection, and substrate structure, the output performance of the RSM-HNG has been significantly enhanced. This technology powers a self-sustaining wireless monitoring system for temperature, humidity, an electronic clock, and road guidance. The RSM-HNG provides continuous energy for smart agriculture, animal husbandry, and environmental monitoring, all driven by wind energy. It holds great potential for regions with abundant wind resources but limited electricity access, offering valuable applications in these areas.

The growing global population and rising concerns about food security highlight the critical need for intelligent agriculture. Among various technologies, plant disease detection is vital but faces challenges in balancing data privacy and model accuracy. To address this, we propose a novel privacy-preserving eggplant disease detection system with high accuracy. First, we introduce a lightweight 3D chaotic cube-based image encryption method that ensures security with low computational cost. Second, a streamlined YOLOv11n-12D framework is employed to optimize detection performance on resource-constrained devices. Finally, the encryption and detection modules are integrated into a real-time, secure, and accurate identification system.Experimental results show our framework achieves near-ideal encryption security (entropy=7.6195, Number of Pixel Change Rate(NPCR)=99.63%, Unified Average Changing Intensity(UACI)=32.85%) with 23× faster encryption (0.0127s) versus existing methods. The distilled YOLOv11n-12D model maintains teacher-level accuracy (mAP@0.5=0.849) at 3.6× the speed of YOLOv12s (2.7ms/inference), with +6.5% mAP improvement for small disease detection (e.g., thrips). This system balances privacy and real-time performance for smart agriculture applications.

This research proposes the design of an autonomous fire detection system based on a Raspberry Pi 3 B+, combined with smoke and flame sensors, enabling real-time monitoring and increased reactivity to fire hazards. To improve detection accuracy and risk prediction, Machine Learning (ML) algorithms, including Random Forest and Linear Regression, were applied to classify hazard levels and anticipate critical situations. The implementation of these models has enabled accurate classification of risk levels, guaranteeing real-time alerts and rapid decision-making. In addition, anomaly detection techniques based on the Random Forest model have been integrated to identify unusual sensor behavior, ensuring the reliability of the data collected and the correction of any measurement errors. A major contribution of this research lies in the fact that the systems were developed to be adaptable in order to serve areas (i.e., rural and agricultural areas without sufficient Internet access and/or cloud infrastructure) that had little or no access to the Internet. By integrating an embedded, independent, and efficient solution, this system offers a viable alternative to conventional monitoring methods. As part of this research, model performance was evaluated using a confusion matrix for classification accuracy, with the identification of abnormal sensor behavior (anomalies). The performance of each model was evaluated by implementing five-fold stratified cross-validation to confirm their accuracy. The logistic regression model yielded an overall average accuracy of 0.9446 ± 0.0600, with an overall F1 score of 0.9173 ± 0.0744 and a total recall of 0.9250 ± 0.0608. On the other hand, the random forest model produced an overall average accuracy of 0.9860 ± 0.0172, with an overall F1 score of 0.9740 ± 0.0319 and a total recall of 0.9733 ± 0.0327. The random forest demonstrated reliable and balanced classification of fire risk levels compared to the other models in this study. The safety and sustainability of agricultural crops are directly supported by the results of this research, with a reduction in agricultural fire risk through the protection of natural resources and increased resilience of farms through better preparedness for natural disasters. The transition of agriculture to smart systems involves integrated smart approaches to agricultural risk management in order to achieve safer, more sustainable, and more resilient agriculture.

According to estimates by the Food and Agriculture Organization of the United Nations (FAO), about a third of all food produced for human consumption in the world is lost or wasted—approximately 1.3 billion tons. Among this, the amount lost during the storage stage is about 15–20% for vegetables and 10–15% for fruits. It is 5–10% for vegetables and fruits during the distribution stage, resulting in a large amount of resource waste and economic losses. At the same time, the global population affected by hunger has reached 828 million, exceeding one-tenth of the total global population. The improvement of the cold chain system will effectively reduce the amount of waste and loss of food during the storage and transportation stages. Firstly, this paper summarizes the concept and development status of traditional preservation technology; environmental parameter sensor components related to fruit and vegetable spoilage in the intelligent cold chain system; the data transmission and processing technology of the intelligent cold chain system, including wireless network communication technology (WI-FI) and cellular mobile communication; short-range communication technology, and the low-power, wide-area network (LPWAN). The smart cold chain system is regulated and optimized through the Internet of Things, blockchain, and digital twin technology to achieve the sustainable development of smart agriculture. The deep integration of artificial intelligence and traditional preservation technology provides new ideas and solutions for the problem of food waste in the world. However, the lack of general standards and the high cost of the intelligent cold chain system are obstacles to the development of the intelligent cold chain system. Governments and researchers at all levels should strive to highly integrate cold chain systems with artificial intelligence technology, establish relevant regulations and standards for cold chain technology, and actively promote development toward intelligence, standardization, and technology.