[Significance] Most current agricultural robots lack the ability to adapt to complex agricultural environments and still have limitations when facing variable, uncertain and unstructured agricultural scenarios. With the acceleration of agricultural intelligent transformation, embodied intelligence, as an intelligent system integrating environment perception, information cognition, autonomous decision-making and action, is giving agricultural robots stronger autonomous perception and complex environment adaptation ability, and becoming an important direction to promote the development of agricultural intelligent robots. In this paper, the technical system and application practice of embodied intelligence are sorted out systematically in the field of agricultural robots, its important value is revealed in improving environmental adaptability, decision-making autonomy and operational flexibility, and theoretical and practical references are provided to promote the development of agricultural robots to a higher level. [Progress] Firstly, the key supporting technologies of embodied intelligent agricultural robots are systematically sorted out, focusing on four aspects, namely, multimodal fusion perception, intelligent autonomous decision-making, autonomous action control and feedback autonomous learning. In terms of multimodal fusion perception, the modular artificial intelligence (AI) algorithm architecture and multimodal large model architecture are summarised. In terms of intelligent autonomous decision-making, two types of approaches based on artificial programming and dedicated task algorithms, and on large-scale pre-trained models are outlined. In terms of autonomous action control, three types of approaches based on the fusion of reinforcement learning and mainstream transformer, large model-assisted reinforcement learning, end-to-end mapping of semantics to action and action end-to-end mapping are summarised. In the area of feedback autonomous learning, the focus is on the related technological advances in the evolution of large model-driven feedback modules. Secondly, it analysed the typical application scenarios of embodied intelligence in agriculture, constructed a technical framework with "embodied perception - embodied cognition - embodied execution - embodied evolution" as the core, and discussed the implementation paths of each module according to the agricultural scenarios. The paths of each module are classified and discussed. Finally, the key technical bottlenecks and application challenges are analysed in depth, mainly including the high complexity of system integration, the significant gap between real and virtual data, and the limited ability of cross-scene generalisation. [Conclusions and Prospects] The future development trend of embodied intelligent agricultural robots is summarised and prospected from the construction of high-quality datasets and simulation platforms, the application of domain large model fusion, and the design of layered collaborative architectures, etc. It mainly focuses on the following aspects. Firstly, the construction of high-quality agricultural scenarios of embodied intelligence datasets is a key prerequisite to realise the embodied intelligence landing in agriculture. The development of embodied intelligent agricultural robots needs to rely on rich and accurate agricultural scene task datasets and highly realistic simulators to support physical interaction and behavioural learning. Secondly, the fusion of basic big model and agricultural domain model is the accelerator of intelligent perception and decision-making of agricultural robots. The in-depth fusion of general basic models in agricultural scenarios will bring stronger perception, understanding and reasoning capabilities to the embodied-intelligent agricultural robots. Thirdly, the "big model high-level planning + small model bottom-level control" architecture is an effective solution to balance intelligence and efficiency. Although large models have advantages in semantic understanding and global strategy planning, their reasoning latency and arithmetic demand can hardly meet the real-time and low-power requirements of agricultural robots. The use of large models for high-level task decomposition, scene semantic parsing and decision making, coupled with lightweight small models or traditional control algorithms to complete the underlying sensory response and motion control, can achieve the complementary advantages of the two.
[Significance] The smart transformation of agricultural product supply chains is an essential solution to the challenges faced by traditional supply chains, such as information asymmetry, high logistics costs, and difficulties in quality traceability. This transformation also serves as a vital pathway to modernize agriculture and enhance industrial competitiveness. By integrating technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI), smart supply chains facilitate precise production and processing, efficient logistics distribution, and transparent quality supervision. As a result, they improve circulation efficiency, ensure product safety, increase farmers' incomes, and promote sustainable agricultural development. Furthermore, in light of global shifts in agricultural trade, this transformation bolsters the international competitiveness of China's agricultural products and propels the agricultural industrial chain toward higher value-added segments. This paper systematically examines the conceptual framework, technological applications, and future trends of smart supply chains, aiming to provide a theoretical foundation for industry practices and insights for policymaking and technological innovation. [Progress] In the production phase, IoT and remote sensing technologies enable real-time monitoring of crop growth conditions, including soil moisture, temperature, and pest infestation, facilitating precision irrigation, fertilization, and pest management. Big data analysis, coupled with AI algorithms, helps in predicting crop yields, optimizing resource allocation, and minimizing waste. Additionally, AI-driven smart pest control systems can dynamically adjust pesticide application, reducing chemical usage and environmental impact. The processing stage leverages advanced technologies for efficient sorting, grading, cleaning, and packaging. Computer vision and hyperspectral imaging technologies enhance the sorting efficiency and quality inspection of agricultural products, ensuring only high-quality products proceed to the next stage. Novel cleaning techniques, such as ultrasonic and nanobubble cleaning, effectively remove surface contaminants and reduce microbial loads without compromising product quality. Moreover, AI-integrated systems optimize processing lines, reduce downtime and enhance overall throughput. Warehousing employs IoT sensors to monitor environmental conditions like temperature, humidity, and gas concentrations, ensure optimal storage conditions for diverse agricultural products. AI algorithms predict inventory demand, optimize stock levels to minimize waste and maximize freshness. Robotics and automation in warehouses improve picking, packing, and palletizing efficiency, reduce labor costs and enhance accuracy. The transportation sector focuses on cold chain innovations to maintain product quality during transit. IoT-enabled temperature-controlled containers and AI-driven scheduling systems ensure timely and efficient delivery. Additionally, the integration of blockchain technology provides immutable records of product handling and conditions, enhances transparency and trust. The adoption of new energy vehicles, such as electric and hydrogen-powered trucks, further reduces carbon footprints and operating costs. In the distribution and sales stages, big data analytics optimize delivery routes, reducing transportation time and costs. AI-powered demand forecasting enables precise inventory management, minimizes stockouts and excess inventories. Moreover, AI and machine learning algorithms personalize marketing efforts, improve customer engagement and satisfaction. Blockchain technology ensures product authenticity and traceability, enhances consumer trust. [Conclusions and Prospects] As technological advancements and societal demands continue to evolve, the smart transformation of agricultural product supply chains has become increasingly urgent. Future development should prioritize unmanned operations to alleviate labor shortages and enhance product quality and safety. Establishing information-sharing platforms and implementing refined management practices are crucial for optimizing resource allocation, improving operational efficiency, and enhancing international competitiveness. Additionally, aligning with the "dual-carbon" strategy by promoting clean energy adoption, optimizing transportation methods, and advocating for sustainable packaging will drive the supply chain toward greater sustainability. However, the application of emerging technologies in agricultural supply chains faces challenges such as data governance, technical adaptability, and standardization. Addressing these issues requires policy guidance, technological innovation, and cross-disciplinary collaboration. By overcoming these challenges, the comprehensive intelligent upgrade of agricultural product supply chains can be achieved, ultimately contribute to the modernization and sustainable development of the agricultural sector.
[Significance] To provide a reference for advancing high-quality agricultural production driven by data, this paper focuses on the issues of inconsistent acquisition standards, incomplete data collection, and ambiguous governance mechanisms in China's agricultural production data, examines existing governance models for agricultural production big data, and clarifies the technical pathways for realizing the value of data elements through the integrated and innovative application of key big data governance technologies and tools in practical scenarios. [Progress] From the perspective of agricultural production big data governance, this paper explores 17 types of big data governance technologies and tools across six core processes: Data acquisition and processing, data storage and exchange, data management, data analysis, large models, and data security guarantee. It conducts in-depth research on the application methods of big data governance technologies in agricultural production, revealing that: Remote sensing, unmanned aerial vehicle(UAV), Internet of Things (IoT), and terminal data acquisition and processing systems are already reatively mature; data storage and exchange system are developing rapidly, data management technologies remain in the initial stage; data analysis technologies have been widely applied; large model technology systems have taken initially shape; and data security assurance systems are gradually being into parctice. The above technologies are effectively applied in scenarios through tools and middleware such as data matching, computing power matching, network adaptation, model matching, scenario matching, and business configuration. This paper also analyzes the data governance throughout the entire agricultural production chain, including pre-production, in-production, and post-production, stages, as well as service cases involving different types of agricultural parks, research institutes and universities, production entities, and farmers. It demonstrates that sound data governance can provide sufficient planning and input analysis prior to production, helping planting entities in making rational plans. In production, it can provide data-driven guidance for key scenarios such as agricultural machinery operations and agricultural technical services, thereby fully supporting decision-making in the production process; and based on massive data, it can achieve reliable results in yield assessment and production benefit evaluation. Additionally, the paper introduces governance experience from national-level industrial parks, provincial-level agricultural science and technology parks, and some single-product entities, and investigates domestic and international technologies, practices, and tools related to agricultural production big data governance, indicating that there is a need to break through the business chains and service model of agricultural production across regions, themes, and scenarios. [Conclusions and Prospects] This paper presents insights into the future development directions of agricultural production big data governance, encompassing the promotion of standard formulation and implementation for agricultural production big data governance, the establishment of a universal resource pool for such governance, the expansion of diversified application scenarios, adaptation to the new paradigm of large-model- and massive-data-driven agricultural production big data governance, and the enhancement of security and privacy protection for agricultural production big data.
[Significance] Artificial intelligence for science (AI4S), as an emerging paradigm that deeply integrates artificial intelligence(AI) with scientific research, has triggered profound transformations in research methodologies. By accelerating scientific discovery through AI technologies, it is driving a shift in scientific research from traditional approaches reliant on experience and intuition to methodologies co-driven by data and AI. This transition has spurred innovative breakthroughs across numerous scientific domains and presents new opportunities for the transformation of agricultural research. With its powerful capabilities in data processing, intelligent analysis, and pattern recognition, AI can transcend the cognitive limitations of researchers in the field and is gradually emerging as an indispensable tool in modern agricultural scientific research, injecting new impetus into the intelligent, efficient, and collaborative development of agricultural scientific research. [Progress] This paper systematically reviews the current advancements in AI4S and its implications for agricultural research. It reveals that AI4S has triggered a global race among countries around the world vying for the commanding heights of a new round of scientific and technological strategies. Developed nations in Europe and America, for instance, have laid out the frontier areas in AI4S and rolled out relevant policies. Meanwhile, some top universities and research institutions are accelerating related research, and tech giants are actively cultivating related industries to advance the application and deployment of AI technologies in scientific research. In recent years, AI4S has achieved remarkable development, showing great potential across multiple disciplines and finding widespread application in data mining, model construction, and result prediction. In the field of agricultural scientific research, AI4S has played an important role in accelerating multi-disciplinary integration, promoting the improvement of the scientific research efficiency, facilitating the breakthrough of complex problems, driving the transformation of the scientific research paradigm, and upgrading scientific research infrastructure. The continuous progress of information technology and synthetic biology has made the interdisciplinary integration of agriculture and multiple disciplines increasingly closer. The deep integration of AI and agricultural scientific research not only improves the application level of AI in the agricultural field but also drives the transformation of traditional agricultural scientific research models towards intelligence, data-driven, and collaborative directions, providing new possibilities for agricultural scientific and technological innovation. The new agricultural digital infrastructure is characterized by intelligent data collection, edge computing power deployment, high-throughput network transmission, and distributed storage architecture, aiming to break through the bottlenecks of traditional agricultural scientific research facilities in terms of real-time performance, collaboration, and scalability. Taking emerging disciplines such as Agrinformatics and climate-focused Agriculture-Forestry-AI (AgFoAI) as examples, they focus on using AI technology to analyze agricultural data, construct crop growth models, and climate change models, etc., to promote the development and innovation of agricultural scientific research. [Conclusions and Prospects] With its robust capabilities in data processing, intelligent analysis, and pattern recognition, AI is increasingly becoming an indispensable tool in modern agricultural scientific research. To address emerging demands, core domains, and research processes in agricultural research, the concept of agricultural intelligent research is proposed, characterized by human-machine collaboration and interdisciplinary integration. This paradigm employs advanced data analytics, pattern recognition, and predictive modeling to perform in-depth mining and precise interpretation of multidimensional, full-lifecycle, large-scale agricultural datasets. By comprehensively unraveling the intrinsic complexities and latent patterns of research subjects, it autonomously generates novel, scientifically grounded, and high-value research insights, thereby driving agricultural research toward greater intelligence, precision, and efficiency. The framework's core components encompass big science infrastructure (supporting large-scale collaborative research), big data resources (integrating heterogeneous agricultural datasets), advanced AI model algorithms (enabling complex simulations and predictions), and collaborative platforms (facilitating cross-disciplinary and cross-institutional synergy). Finally, in response to challenges related to data resources, model capabilities, research ecosystems, and talent development, actionable pathways and concrete recommendations are outlined from the perspectives of top-level strategic planning, critical technical ecosystems, collaborative innovation ecosystems, disciplinary system construction, and interdisciplinary talent cultivation, aiming to establish a new AI4S-oriented agricultural research framework.
[Significance] Estrus monitoring and identification in cows is a crucial aspect of breeding management in beef and dairy cattle farming. Innovations in precise sensing and intelligent identification methods and technologies for estrus in cows are essential not only for scientific breeding, precise management, and smart breeding on a population level but also play a key supportive role in health management, productivity enhancement, and animal welfare improvement at the individual level. The aims are to provide a reference for scientific management and the study of modern production technologies in the beef and dairy cattle industry, as well as theoretical methodologies for the research and development of key technologies in precision livestock farming. [Progress] Based on describing the typical characteristics of normal and abnormal estrus in cows, this paper systematically categorizes and summarizes the recent research progress, development trends, and methodological approaches in estrus monitoring and identification technologies, focusing on the monitoring and diagnosis of key physiological signs and behavioral characteristics during the estrus period. Firstly, the paper outlines the digital monitoring technologies for three critical physiological parameters, body temperature, rumination, and activity levels, and their applications in cow estrus monitoring and identification. It analyzes the intrinsic reasons for performance bottlenecks in estrus monitoring models based on body temperature, compares the reliability issues faced by activity-based estrus monitoring, and addresses the difficulties in balancing model generalization and robustness design. Secondly, the paper examines the estrus sensing and identification technologies based on three typical behaviors: feeding, vocalization, and sexual desire. It highlights the latest applications of new artificial intelligence technologies, such as computer vision and deep learning, in estrus monitoring and points out the critical role of these technologies in improving the accuracy and timeliness of monitoring. Finally, the paper focuses on multi-factor fusion technologies for estrus perception and identification, summarizing how different researchers combine various physiological and behavioral parameters using diverse monitoring devices and algorithms to enhance accuracy in estrus monitoring. It emphasizes that multi-factor fusion methods can improve detection rates and the precision of identification results, being more reliable and applicable than single-factor methods. The importance and potential of multi-modal information fusion in enhancing monitoring accuracy and adaptability are underlined. The current shortcomings of multi-factor information fusion methods are analyzed, such as the potential impact on animal welfare from parameter acquisition methods, the singularity of model algorithms used for representing multi-factor information fusion, and inadequacies in research on multi-factor feature extraction models and estrus identification decision algorithms. [Conclusions and Prospects] From the perspectives of system practicality, stability, environmental adaptability, cost-effectiveness, and ease of operation, several key issues are discussed that need to be addressed in the further research of precise sensing and intelligent identification technologies for cow estrus within the context of high-quality development in digital livestock farming. These include improving monitoring accuracy under weak estrus conditions, overcoming technical challenges of audio extraction and voiceprint construction amidst complex background noise, enhancing the adaptability of computer vision monitoring technologies, and establishing comprehensive monitoring and identification models through multi-modal information fusion. It specifically discusses the numerous challenges posed by these issues to current technological research and explains that future research needs to focus not only on improving the timeliness and accuracy of monitoring technologies but also on balancing system cost-effectiveness and ease of use to achieve a transition from the concept of smart farming to its practical implementation.
[Significance] Plant active small molecules play an indispensable role in plants. They form the basis of the core physiological mechanisms that regulate the plant growth and development and enhance resilience to environmental stress. Achieving highly precise quantitative analysis of these active small molecules is therefore vital for promoting precise management practice in the agricultural and accelerating the development of smart agriculture. Currently, various technologies exist for the detection and analysis of these small molecules in plants. Among them, electrochemical sensing platforms have attracted extensive attention due to their significant advantages, including high sensitivity, excellent selectivity, and low cost. These advantages enable them to effectively detect trace levels of various active small molecules in plant samples. They also have the potential for real-time and in-situ detection. [Progress] Based on a comprehensive review of relevant academic literature, this article systematically summarizes the current research progress and status of electrochemical sensors applied in detecting plant active small molecule. Based on this, the article further analyzes the core sensing mechanisms of different electrochemical sensors types, signal amplification technologies for enhancing detection performance, and their huge potential in practical applications. Furthermore, this paper explores a notable development direction in this field: Sensor technology is evolving from the traditional in vitro detection mode to more challenging in vivo detection and in-situ real-time monitoring methods. Meanwhile, the article particularly emphasizes and elaborates in detail the indispensable and significant role of nanomaterials in key links such as constructing high-performance sensing interfaces and significantly enhancing detection sensitivity and selectivity. Finally, it prospectively discusses the innovative integration of electrochemical sensors with cutting-edge flexible electronic technology and powerful artificial intelligence (AI)-based data analysis, along with their potential for broad application. [Conclusions and Prospects] This article comprehensively identifies and summarizes the core technical challenges that electrochemical sensors currently face in detecting plant active small molecule. In terms of environmental detection, due to the influence of the complex matrix within plants, the response signal of the sensor is prone to drift, and its stability and sensitivity show a decline. Regarding electrolytes, the external application of liquid electrolytes dilutes the target molecules concentration in plant samples, lowering the detection accuracy. Furthermore, the transition from principle development to mature productization and industrialization of electrochemical sensors is relatively lengthy, and there are few types of sensors available for the detection of plant physiological indicators: Limiting their application in actual agricultural production. On this basis, the article prospectively analyzes the key directions of future research. First, continuously improving sensor performance indicators such as sensitivity, selectivity and reliability. Second, exploring and optimizing electrolyte material systems with stronger adaptability to significantly improve detection accuracy and long-term stability. Third, promoting deeper integration and innovation of sensor technology with advanced micro-nano electronic technology and powerful AI algorithms. The core objective of this review is to provide a theoretical guidance framework for in-depth research and systematic performance optimization of electrochemical sensing technology for plant active small molecules, as well as practical guidance for the actual application of related sensors in complex plant substrate environments.
[Significance] The efficient and precise identification of rice growth stages through remote sensing technology holds critical significance for varietal breeding optimization and production management enhancement. Remote sensing, characterized by high spatial-temporal resolution and automated monitoring capabilities, provides transformative solutions for large-scale dynamic phenology monitoring, offering essential technical support to address climate change impacts and food security challenges in complex agroecosystems where precise monitoring of growth stage transitions enables yield prediction and stress-resilient cultivation management. [Progress] In recent years, the technical system for monitoring rice growth stages has achieved systematic breakthroughs in the perception layer, decision-making layer, and execution layer, forming a technological ecosystem covering the entire chain of "data acquisition-feature analysis-intelligent decision-making-precise operation". At the perception layer, a "space-air-ground" three-dimensional monitoring network has been constructed: High-altitude satellites (Sentinel-2, Landsat) realize regional-scale phenological dynamic tracking through wide-spectrum multi-temporal observations; low-altitude unmanned aerial vehicle (UAV) equipped with hyperspectral and light detection and ranging (LiDAR) sensors analyze the heterogeneity of canopy three-dimensional structure; near-ground sensor networks real-timely capture leaf-scale photosynthetic efficiency and nitrogen metabolism parameters. Radiometric calibration and temporal interpolation algorithms eliminate the spatio-temporal heterogeneity of multi-source data, forming continuous and stable monitoring capabilities. Innovations in technical methods show three integration trends: Firstly, multimodal data collaboration mechanisms break through the physical characteristic barriers between optical and radar data; secondly, deep integration of mechanistic models and data-driven approaches embeds the scattering by arbitrarily inclined leaves by arbitrary inclined leaves (PROSPECT + SAIL, PROSAIL) radiative transfer model into the long short-term memory (LSTM) network architecture; thirdly, cross-scale feature analysis technology breaks through by constructing organ-population association models based on dynamic attention mechanisms, realizing multi-granularity mapping between panicle texture features and canopy leaf area index (LAI) fluctuations. The current technical system has completed three-dimensional leaps: From discrete manual observations to full-cycle continuous perception, with monitoring frequency upgraded from weekly to hourly; from empirical threshold-based judgment to mechanism-data hybrid-driven, the cross-regional generalization ability of the model can be significantly improved; from independent link operations to full-chain collaboration of "perception-decision-execution", constructing a digital management closed-loop covering rice sowing to harvest, providing core technical support for smart farm construction. [Conclusions and Prospects] Current technologies face three-tiered challenges in data heterogeneity, feature limitations and algorithmic constraints. Future research should focus on three aspects: 1) Multi-source data assimilation systems to reconcile spatiotemporal heterogeneity through UAV-assisted satellite calibration and GAN-based cloud-contaminated data reconstruction; 2) Cross-scale physiological-spectral models integrating 3D canopy architecture with adaptive soil-adjusted indices to overcome spectral saturation; 3) Mechanism-data hybrid paradigms embedding thermal-time models into LSTM networks for environmental adaptation, developing lightweight CNNs with multi-scale attention for occlusion-resistant panicle detection, and implementing transfer learning for cross-regional model generalization. The convergence of multi-source remote sensing, intelligent algorithms, and physiological mechanisms will establish a full-cycle dynamic monitoring system based on agricultural big data.
[Significance] Grass damage in farmland seriously restricts the quality and yield of crop planting and production, and promotes the occurrence of pests and diseases. Weed control is a necessary measure for high yield and high quality of crops. Currently, there are five main weed control methods: Manual, biological, thermal, mechanical, and chemical weed control. Traditional chemical weed control methods are gradually limited due to soil pollution and ecological balance disruption. Intelligent laser weeding technology, with the characteristics of environmental protection, high efficiency, flexibility, and automation, as an emerging and promising ecological and environmental protection new object control method for field weeds, has become the core direction to replace chemical weeding in recent years. The laser weeding robot is the carrier of laser weeding technology, an important manifestation of the development of modern agriculture towards intelligence and precision, and has great application and promotion value. [Progress] Laser weeding is currently a research hotspot to develop and study key technologies and equipment for smart agriculture, and has achieved a series of significant results, greatly promoting the promotion and application of intelligent laser weeding robots in the field. Laser weed control technology achieves precise weed control through thermal, photochemical, and photodynamic effects. In this article, the research background of laser weeding was introduced, its key technologies, operation system and equipment were discussed in details, covering aspects such as operating principles, system architecture, seedling, weed recognition and localization, robot navigation and path planning, as well as actuator control technologies. Then, based on the current research status of laser weeding robots, the existing problems and development trends of intelligent laser weeding robots were prospected. [Conclusion and Prospect] Based on the different field grass conditions in different regions, a large number of indoor and outdoor experiments on laser weed control should be carried out in the future to further verify the technical effectiveness and feasibility of laser field weed control, providing support for the research and application of laser weed control equipment technology. Despite facing challenges such as high costs and poor environmental adaptability, with the integration of technologies such as artificial intelligence and the Internet of Things, as well as policy support, laser weeding is expected to become an important support for sustainable agricultural development.
[Significance] Developing new-quality productivity is of great significance for promoting high-quality development of animal husbandry. However, there is currently limited research on new-quality productivity in animal husbandry, and there is a lack of in-depth analysis on its connotation, characteristics, constraints, and promotion path. [Progress] This article conducts a systematic study on the high-quality development of animal husbandry productivity driven by artificial intelligence. The new-quality productivity of animal husbandry is led by cutting-edge technological innovations such as biotechnology, information technology, and green technology, with digitalization, greening, and ecologicalization as the direction of industrial upgrading. Its basic connotation is manifested as higher quality workers, more advanced labor materials, and a wider range of labor objects. Compared with traditional productivity, the new-quality productivity of animal husbandry is an advanced productivity guided by technological innovation, new development concepts, and centered on the improvement of total factor productivity. It has significant characteristics of high production efficiency, good industrial benefits, and strong sustainable development capabilities. China's new-quality productivity in animal husbandry has a good foundation for development, but it also faces constraints such as insufficient innovation in animal husbandry breeding technology, weak core competitiveness, low mechanization rate of animal husbandry, weak independent research and development capabilities of intelligent equipment, urgent demand for "machine replacement", shortcomings in the quantity and quality of animal husbandry talents, low degree of scale of animal husbandry, and limited level of intelligent management. Artificial intelligence in animal husbandry can be widely used in environmental control, precision feeding, health monitoring and disease prevention and control, supply chain optimization and other fields. Artificial intelligence, through revolutionary breakthroughs in animal husbandry technology represented by digital technology, innovative allocation of productivity factors in animal husbandry linked by data elements, and innovative allocation of productivity factors in animal husbandry adapted to the digital economy, has given birth to new-quality productivity in animal husbandry and empowered the high-quality development of animal husbandry. [Conclusions and Prospects] This article proposes a path to promote the development of new-quality productivity in animal husbandry by improving the institutional mechanism of artificial intelligence to promote the development of modern animal husbandry industry, strengthening the application of artificial intelligence in animal husbandry technology innovation and promotion, and improving the management level of artificial intelligence in the entire industry chain of animal husbandry.
[Significance] With the rapid development of robotics technology and the persistently rise of labor costs, the application of robots in facility agriculture is becoming increasingly widespread. These robots can enhance operational efficiency, reduce labor costs, and minimize human errors. However, the complexity and diversity of facility environments, including varying crop layouts and lighting conditions, impose higher demands on robot navigation. Therefore, achieving stable, accurate, and rapid navigation for robots has become a key issue. Advanced sensor technologies and algorithms have been proposed to enhance robots' adaptability and decision-making capabilities in dynamic environments. This not only elevates the automation level of agricultural production but also contributes to more intelligent agricultural management. [Progress] This paper reviews the key technologies of automatic navigation for facility agricultural robots. It details beacon localization, inertial positioning, simultaneous localization and mapping (SLAM) techniques, and sensor fusion methods used in autonomous localization and mapping. Depending on the type of sensors employed, SLAM technology could be subdivided into vision-based, laser-based and fusion systems. Fusion localization is further categorized into data-level, feature-level, and decision-level based on the types and stages of the fused information. The application of SLAM technology and fusion localization in facility agriculture has been increasingly common. Global path planning plays a crucial role in enhancing the operational efficiency and safety of facility aricultural robots. This paper discusses global path planning, classifying it into point-to-point local path planning and global traversal path planning. Furthermore, based on the number of optimization objectives, it was divided into single-objective path planning and multi-objective path planning. In regard to automatic obstacle avoidance technology for robots, the paper discusses sevelral commonly used obstacle avoidance control algorithms commonly used in facility agriculture, including artificial potential field, dynamic window approach and deep learning method. Among them, deep learning methods are often employed for perception and decision-making in obstacle avoidance scenarios. [Conclusions and Prospects] Currently, the challenges for facility agricultural robot navigation include complex scenarios with significant occlusions, cost constraints, low operational efficiency and the lack of standardized platforms and public datasets. These issues not only affect the practical application effectiveness of robots but also constrain the further advancement of the industry. To address these challenges, future research can focus on developing multi-sensor fusion technologies, applying and optimizing advanced algorithms, investigating and implementing multi-robot collaborative operations and establishing standardized and shared data platforms.
[Significance] Fruit-picking robot stands as a crucial solution for achieving intelligent fruit harvesting. Significant progress has been made in developing foundational methods for picking robots, such as fruit recognition, orchard navigation, path planning for picking, and robotic arm control, the practical implementation of a seamless picking system that integrates sensing, movement, and picking capabilities still encounters substantial technical hurdles. In contrast to current picking systems, the next generation of fruit-picking robots aims to replicate the autonomous skills exhibited by human fruit pickers. This involves effectively performing ongoing tasks of perception, movement, and picking without human intervention. To tackle this challenge, this review delves into the latest research methodologies and real-world applications in this field, critically assesses the strengths and limitations of existing methods and categorizes the essential components of continuous operation into three sub-modules: local target recognition, global mapping, and operation planning. [Progress] Initially, the review explores methods for recognizing nearby fruit and obstacle targets. These methods encompass four main approaches: low-level feature fusion, high-level feature learning, RGB-D information fusion, and multi-view information fusion, respectively. Each of these approaches incorporates advanced algorithms and sensor technologies for cluttered orchard environments. For example, low-level feature fusion utilizes basic attributes such as color, shapes and texture to distinguish fruits from backgrounds, while high-level feature learning employs more complex models like convolutional neural networks to interpret the contextual relationships within the data. RGB-D information fusion brings depth perception into the mix, allowing robots to gauge the distance to each fruit accurately. Multi-view information fusion tackles the issue of occlusions by combining data from multiple cameras and sensors around the robot, providing a more comprehensive view of the environment and enabling more reliable sensing. Subsequently, the review shifts focus to orchard mapping and scene comprehension on a broader scale. It points out that current mapping methods, while effective, still struggle with dynamic changes in the orchard, such as variations of fruits and light conditions. Improved adaptation techniques, possibly through machine learning models that can learn and adjust to different environmental conditions, are suggested as a way forward. Building upon the foundation of local and global perception, the review investigates strategies for planning and controlling autonomous behaviors. This includes not only the latest advancements in devising movement paths for robot mobility but also adaptive strategies that allow robots to react to unexpected obstacles or changes within the whole environment. Enhanced strategies for effective fruit picking using the Eye-in-Hand system involve the development of more dexterous robotic hands and improved algorithms for precisely predicting the optimal picking point of each fruit. The review also identifies a crucial need for further advancements in the dynamic behavior and autonomy of these technologies, emphasizing the importance of continuous learning and adaptive control systems to improve operational efficiency in diverse orchard environments. [Conclusions and Prospects] The review underscores the critical importance of coordinating perception, movement, and picking modules to facilitate the transition from a basic functional prototype to a practical machine. Moreover, it emphasizes the necessity of enhancing the robustness and stability of core algorithms governing perception, planning, and control, while ensuring their seamless coordination which is a central challenge that emerges. Additionally, the review raises unresolved questions regarding the application of picking robots and outlines future trends, include deeper integration of stereo vision and deep learning, enhanced global vision sampling, and the establishment of standardized evaluation criteria for overall operational performance. The paper can provide references for the eventual development of robust, autonomous, and commercially viable picking robots in the future.
[Significance] Building the agricultural new quality productivity is of great significance. It is the advanced quality productivity which realizes the transformation, upgrading, and deep integration of substantive, penetrating, operational, and media factors, and has outstanding characteristics such as intelligence, greenness, integration, and organization. As a new technology revolution in the field of agriculture, smart agricultural technology transforms agricultural production mode by integrating agricultural biotechnology, agricultural information technology, and smart agricultural machinery and equipment, with information and knowledge as important core elements. The inherent characteristics of "high-tech, high-efficiency, high-quality, and sustainable" in agricultural new quality productivity are fully reflected in the practice of smart agricultural technology innovation. And it has become an important core and engine for promoting the agricultural new quality productivity. [Progress] Through literature review and theoretical analysis, this article conducts a systematic study on the practical foundation, internal logic, and problem challenges of smart agricultural technology innovation leading the development of agricultural new quality productivity. The conclusions show that: (1) At present, the global innovation capability of smart agriculture technology is constantly enhancing, and significant technology breakthroughs have been made in fields such as smart breeding, agricultural information perception, agricultural big data and artificial intelligence, smart agricultural machinery and equipment, providing practical foundation support for leading the development of agricultural new quality productivity. Among them, the smart breeding of 'Phenotype+Genotype+Environmental type' has entered the fast lane, the technology system for sensing agricultural sky, air, and land information is gradually maturing, the research and exploration on agricultural big data and intelligent decision-making technology continue to advance, and the creation of smart agricultural machinery and equipment for different fields has achieved fruitful results; (2) Smart agricultural technology innovation provides basic resources for the development of agricultural new quality productivity through empowering agricultural factor innovation, provides sustainable driving force for the development of agricultural new quality productivity through empowering agricultural technology innovation, provides practical paradigms for the development of agricultural new quality productivity through empowering agricultural scenario innovation, provides intellectual support for the development of agricultural new quality productivity through empowering agricultural entity innovation, and provides important guidelines for the development of agricultural new quality productivity through empowering agricultural value innovation; (3) Compared to the development requirements of agricultural new quality productivity in China and the advanced level of international smart agriculture technology, China's smart agriculture technology innovation is generally in the initial stage of multi-point breakthroughs, system integration, and commercial application. It still faces major challenges such as an incomplete policy system for technology innovation, key technologies with bottlenecks, blockages and breakpoints, difficulties in the transformation and implementation of technology achievements, and incomplete support systems for technology innovation. [Conclusions and Prospects] Regarding the issue of technology innovation in smart agriculture, this article proposes the 'Four Highs' path of smart agriculture technology innovation to fill the gaps in smart agriculture technology innovation and accelerate the formation of agricultural new quality productivity in China. The "Four Highs" path specifically includes the construction of high-energy smart agricultural technology innovation platforms, the breakthroughs in high-precision and cutting-edge smart agricultural technology products, the creation of high-level smart agricultural application scenarios, and the cultivation of high-level smart agricultural innovation talents. Finally, this article proposes four strategic suggestions such as deepening the understanding of smart agriculture technology innovation and agricultural new quality productivity, optimizing the supply of smart agriculture technology innovation policies, building a national smart agriculture innovation development pilot zone, and improving the smart agriculture technology innovation ecosystem.
[Significance] With the escalating global climate change and ecological pollution issues, the "dual carbon" target of Carbon Peak and Carbon Neutrality has been incorporated into various sectors of China's social development. To ensure the green and sustainable development of agriculture, it is imperative to minimize energy consumption and reduce pollution emissions at every stage of agricultural mechanization, meet the diversified needs of agricultural machinery and equipment in the era of intelligent information, and develop low-carbon agricultural mechanization. The development of low-carbon agricultural mechanization is not only an important part of the transformation and upgrading of agricultural mechanization in China but also an objective requirement for the sustainable development of agriculture under the "dual carbon" target. Progress] The connotation and objectives of low-carbon agricultural mechanization are clarified and the development logic of low-carbon agricultural mechanization from three dimensions: theoretical, practical, and systematic are expounded. The "triple-win" of life, production, and ecology is proposed, it is an important criterion for judging the functional realization of low-carbon agricultural mechanization system from a theoretical perspective. The necessity and urgency of low-carbon agricultural mechanization development from a practical perspective is revealed. The "human-machine-environment" system of low-carbon agricultural mechanization development is analyzed and the principles and feasibility of coordinated development of low-carbon agricultural mechanization based on a systemic perspective is explained. Furthermore, the deep-rooted reasons affecting the development of low-carbon agricultural mechanization from six aspects are analyzed: factor conditions, demand conditions, related and supporting industries, production entities, government, and opportunities. Conclusion and Prospects] Four approaches are proposed for the realization of low-carbon agricultural mechanization development: (1) Encouraging enterprises to implement agricultural machinery ecological design and green manufacturing throughout the life cycle through key and core technology research, government policies, and financial support; (2) Guiding agricultural entities to implement clean production operations in agricultural mechanization, including but not limited to innovative models of intensive agricultural land, exploration and promotion of new models of clean production in agricultural mechanization, and the construction of a carbon emission measurement system for agricultural low-carbonization; (3) Strengthening the guidance and implementation of the concept of socialized services for low-carbon agricultural machinery by government departments, constructing and improving a "8S" system of agricultural machinery operation services mainly consisting of Sale, Spare part, Service, Survey, Show, School, Service, and Scrap, to achieve the long-term development of dematerialized agricultural machinery socialized services and green shared operation system; (4) Starting from concept guidance, policy promotion, and financial support, comprehensively advancing the process of low-carbon disposal and green remanufacturing of retired and waste agricultural machinery by government departments.
