Search Results (85)

The Solar Agro Savior (SAS) is an innovative solution that is assisted by drones for the sustainable utilization of water and plant disease observation in the agriculture sector. This system integrates an alerting mechanism for humidity, moisture, and temperature variations, which affect the plants’ health and optimization in water utilization, which enhances plant yield productivity. A significant feature of the system is the efficient monitoring system in a larger region through drones’ high-resolution cameras, which enables real-time, efficient response and alerting for environmental fluctuations to the authorities. The machine learning algorithm, particularly recurrent neural networks, which is a pioneer with agriculture and pest control, is incorporated for intelligent monitoring systems. The proposed system incorporates a specialized form of a recurrent neural network, Long Short-Term Memory (LSTM), which effectively addresses the vanishing gradient problem. It also utilizes an attention-based mechanism that enables the model to assign meaningful weights to the most important parts of the data sequence. This algorithm not only enhances water utilization efficiency but also boosts plant yield and strengthens pest control mechanisms. This system also provides sustainability through the re-utilization of water and the elimination of electric energy through solar panel systems for powering the inbuilt irrigation system. A comparative analysis of variant algorithms in the agriculture sector with a machine learning approach was also illustrated, and the proposed system yielded 99% yield accuracy, a 97.8% precision value, 98.4% recall, and a 98.4% F1 score value. By encompassing solar irrigation and artificial intelligence-driven analysis, the proposed algorithm, Solar Argo Savior, established a sustainable framework in the latest agricultural sectors and promoted sustainability to protect our environment and community. Full article

Seedling transplantation, a pivotal component in advancing the cultivation of vegetables and cash crops, significantly bolsters crops’ resilience against drought, cold, pests, and diseases, while substantially enhancing yields. The implementation of transplanting machinery not only remarkably alleviates the labor-intensive nature of transplantation but also elevates the precision and uniformity of the process, thereby facilitating mechanized plant protection and harvesting operations. This article summarizes the research status and development trends of mechanized field transplanting technology and equipment. It also analyzes and summarizes the types and current status of typical representative automatic seedling picking mechanisms. Based on the current research status, the challenges of mechanized transplanting technology were analyzed, mainly the following: insufficient integration of agricultural machinery and agronomy; the standards for each stage of transplanting are not perfect; the adaptability of existing transplanting machines is poor; the level of informatization and intelligence of equipment is low; the lack of innovation in key components, such as seedling picking and transplanting mechanisms; and the proposed solutions to address the issues. Corresponding solutions are proposed, such as the following: strengthening interdisciplinary collaboration; establishing standards for transplanting processes; enhancing transplanter adaptability; accelerating intelligentization and digitalization of transplanters; strengthening the theoretical framework; and performance optimization of transplanting mechanisms. Finally, the development direction of future fully automatic transplanting machines was discussed, including the following: improving the transplanting efficiency and quality of transplanting machines; integrating research and development of testing, planting, and seedling supplementation for transplanting machines; unmanned transplanting operations; and fostering collaborative industrial development. Full article

Although many plant families are predominantly mycorrhizal, few symbiotic relationships between plants and arbuscular mycorrhizal fungi (AMF) have been thoroughly studied. Mycorrhized plants tend to exhibit greater tolerance to soil-borne pathogens and enhanced plant defence. Legumes, including common bean (Phaseolus vulgaris L.), are essential sources of protein globally. To improve common bean productivity, identifying efficient native microsymbionts is crucial. This study aimed to identify native AMF associated with common bean roots that could act as biostimulants and protect against soil diseases under varying environmental conditions. Agronomic trials were conducted at MBG-CSIC (Pontevedra, Spain) in 2021 and 2022, testing combinations of nitrogen fertilization, Burkholderia alba, Trichoderma harzianum, and a control. Traits such as nodulation, biomass, plant vigor, disease severity, nutrient content, and yield were evaluated. Four AMF species across three genera were identified. No consistent pattern was observed in AMF influence on agronomic traits. However, reduced mycorrhization in 2022 was associated with decreased nodulation, likely due to higher temperatures. Surprisingly, yields were higher in 2022 despite lower colonization. These findings suggest that intelligent use of AMF could reduce pesticide use, enhance sustainability, and promote healthier food systems. Continued research and conservation efforts are essential to optimize their benefits in legume production. Full article

Precise detection of canopy parameters is vital as it offers essential information for pest management in orchards. Among these parameters, leaf area density stands out as a key indicator of orchard canopies. A detection algorithm for leaf area density was proposed, and a leaf area density detection system for orchard canopies was designed based on the algorithm. By processing the point cloud data acquired by using LiDAR together with the algorithm, the total leaf area of the fitted leaves was calculated. Through an orthogonal regression experiment conducted on a laboratory-simulated canopy, this research established a mathematical calculation model ($R^2$ = 0.96) for determining the leaf area density of an orchard canopy. The leaf area density of an orchard canopy can be calculated using the total leaf area of the fitted leaves and an established mathematical model. To assess the accuracy of the detection system, both laboratory-simulated canopy experiments and real orchard canopy experiments were conducted. The results revealed that the absolute value of the mean relative error in the laboratory-simulated canopy experiments was 11.58%, and the absolute value of the mean relative error in the orchard canopy experiments was 16.75%. The research results have confirmed the feasibility of the LiDAR point cloud data processing algorithm. Furthermore, this algorithm can provide theoretical support for the subsequent development of intelligent plant protection equipment in orchards. Full article

With the advancement of Agriculture 4.0 and the ongoing transition toward sustainable and intelligent agricultural systems, deep learning-based multimodal fusion technologies have emerged as a driving force for crop monitoring, plant management, and resource conservation. This article systematically reviews research progress from three perspectives: technical frameworks, application scenarios, and sustainability-driven challenges. At the technical framework level, it outlines an integrated system encompassing data acquisition, feature fusion, and decision optimization, thereby covering the full pipeline of perception, analysis, and decision making essential for sustainable practices. Regarding application scenarios, it focuses on three major tasks—disease diagnosis, maturity and yield prediction, and weed identification—evaluating how deep learning-driven multisource data integration enhances precision and efficiency in sustainable farming operations. It further discusses the efficient translation of detection outcomes into eco-friendly field practices through agricultural navigation systems, harvesting and plant protection robots, and intelligent resource management strategies based on feedback-driven monitoring. In addressing challenges and future directions, the article highlights key bottlenecks such as data heterogeneity, real-time processing limitations, and insufficient model generalization, and proposes potential solutions including cross-modal generative models and federated learning to support more resilient, sustainable agricultural systems. This work offers a comprehensive three-dimensional analysis across technology, application, and sustainability challenges, providing theoretical insights and practical guidance for the intelligent and sustainable transformation of modern agriculture through multimodal fusion. Full article

To address the challenges of low pesticide utilization, insufficient automation, and health risks in orchard plant protection, we developed an autonomous spraying vehicle using ArduPilot firmware and a robot operating system (ROS). The system tackles orchard navigation hurdles, including global navigation satellite system (GNSS) signal obstruction, light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) error accumulation, and lighting-limited visual positioning. A key innovation is the integration of an extended Kalman filter (EKF) to dynamically fuse T265 visual odometry, inertial measurement unit (IMU), and GPS data, overcoming single-sensor limitations and enhancing positioning robustness in complex environments. Additionally, the study optimizes PID controller derivative parameters for tracked chassis, improving acceleration/deceleration control smoothness. The system, composed of Pixhawk 4, Raspberry Pi 4B, Silan S2L LIDAR, T265 visual odometry, and a Quectel EC200A 4G module, enables autonomous path planning, real-time obstacle avoidance, and multi-mission navigation. Indoor/outdoor tests and field experiments in Sun Village Orchard validated its autonomous cruising and obstacle avoidance capabilities under real-world orchard conditions, demonstrating feasibility for intelligent plant protection. Full article

This article systematically reviews the research progress and challenges in mechanized planting technology and equipment for wine grapes, with a particular focus on the current status and development of the wine grape industry in China. Studies show that the global wine grape cultivation area is extensive, and China, as one of the major producers, has made significant progress in planting scale and technology application in recent years. However, compared to developed countries such as France and the United States, China still lags behind in the full mechanization of wine grape cultivation, especially in winter cold protection and spring soil clearing. This paper provides a detailed analysis of mechanized operations in wine grape cultivation and compares the differences in related technologies and equipment between China and other countries. The study points out that the main problems faced by China in the mechanized production of wine grapes include a wide variety of equipment, complex winter cold protection procedures, diversified planting patterns, and inadequate technical standards. Future development directions should focus on the integration of advanced technologies with traditional equipment, the construction of a full mechanization technology system, the integration of intelligent and information technologies, and the development of multifunctional composite equipment. By addressing these issues, this article provides a theoretical basis and practical recommendations for the full mechanization development of China’s wine grape industry, aiming to enhance its international competitiveness. Full article

The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute a significant concern in the fields of public health and consumer protection. This review highlights recent strategies and emerging methods aimed at preventing fungal growth and mycotoxin contamination in food matrices as opposed to traditional approaches such as chemical fungicides, which may leave toxic residues and pose risks to human and animal health as well as the environment. The novel methodologies discussed include the use of plant-derived compounds such as essential oils, classified as Generally Recognized as Safe (GRAS), polyphenols, lactic acid bacteria, cold plasma technologies, nanoparticles (particularly metal nanoparticles such as silver or zinc nanoparticles), magnetic materials, and ionizing radiation. Among these, essential oils, polyphenols, and lactic acid bacteria offer eco-friendly and non-toxic alternatives to conventional fungicides while demonstrating strong antimicrobial and antifungal properties; essential oils and polyphenols also possess antioxidant activity. Cold plasma and ionizing radiation enable rapid, non-thermal, and chemical-free decontamination processes. Nanoparticles and magnetic materials contribute advantages such as enhanced stability, controlled release, and ease of separation. Furthermore, this review explores recent advancements in the application of artificial intelligence, particularly machine learning methods, for the identification and classification of fungal species as well as for predicting the growth of toxigenic fungi and subsequent mycotoxin production in food products and culture media. Full article

The integration of machine learning (ML) into self-governing spraying systems is one of the major developments in digital precision agriculture that is significantly improving resource efficiency, sustainability, and production. This study looks at current advances in machine learning applications for automated spraying in agricultural mechanisation, emphasising the new innovations, difficulties, and prospects. This study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics, sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised, and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection, as well as accurate application of agricultural plant protection. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations’ efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advances, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods. Full article

To enhance the accessibility and accuracy of plant protection knowledge for agricultural practitioners, this study develops an intelligent question-answering (QA) system based on a large language model (LLM). A local knowledge base was constructed by vectorizing 7000 research papers and books in the field of plant protection, from which 568 representative papers were selected to generate QA data using an LLM. After data cleaning and filtering, a fine-tuning dataset comprising 9000 question–answer pairs was curated. To optimize the model’s performance, low-rank adaptation (LoRA) was applied to the InterLM-20B model, resulting in the InterLM-20B-LoRA, which was integrated with Langchain-ChatChat and the local knowledge base to develop the QA system. Additionally, retrieval-augmented generation (RAG) technology was implemented to enhance response accuracy by enabling the model to retrieve relevant field-specific knowledge before generating answers, effectively mitigating the risk of hallucinated information. The experimental results demonstrate that the proposed system achieves an answer accuracy of 97%, highlighting its potential as an advanced solution for intelligent agricultural QA services. Full article

Based on the characteristics of small agricultural machinery in terms of flexibility and high efficiency when operating in small plots of hilly and mountainous areas, as well as the demand for improving the automation and intelligence levels of agricultural machinery, this paper conducted research on the path tracking control of the automatic navigation operation of a crawler sprayer. Based on the principles of the kinematic model and the position prediction model of the agricultural machinery chassis, a pure pursuit controller based on adaptive look-ahead distance was designed for the tracked motion chassis. Using a lightweight crawler sprayer as the research platform, integrating onboard industrial control computers, sensors, communication modules, and other hardware, an automatic navigation operation system was constructed, achieving precise control of the crawler sprayer during the path tracking process. Simulation test results show that the path tracking control method based on adaptive look-ahead distance has the characteristics of smooth control and small steady-state error. Field tests indicate that the crawler sprayer exhibits small deviations during path tracking, with an average absolute error of 2.15 cm and a maximum deviation of 4.08 cm when operating at a speed of 0.7 m/s. In the line-following test, with initial position deviations of 0.5 m, 1.0 m, and 1.5 m, the line-following times were 7.45 s, 11.91 s, and 13.66 s, respectively, and the line-following distances were 5.21 m, 8.34 m, and 9.56 m, respectively. The maximum overshoot values were 6.4%, 10.5%, and 12.6%, respectively. The autonomous navigation experiments showed a maximum deviation of 5.78 cm and a mean absolute error of 2.69 cm. The proportion of path deviations within ±5 cm and ±10 cm was 97.32% and 100%, respectively, confirming the feasibility of the proposed path tracking control method. This significantly enhanced the path tracking performance of the crawler sprayer while meeting the requirements for autonomous plant protection spraying operations. Full article

Workplace safety, particularly in the construction industry, is a moral and legal imperative, prioritizing the protection of workers’ health and well-being. In Italy, Legislative Decree 81/08 (and subsequent modifications) serves as a regulatory framework for workplace safety, defining the duties of employers and employees and promoting accident prevention measures. Building information modeling technology, which has revolutionized the global construction industry by offering an integrated approach to design, construction, and management through intelligent digital models, has only recently started gaining traction in Italy as part of Industry 4.0. This article examines the potential of integrating the current prevention strategies with BIM technology to optimize safety design on construction sites. A case study demonstrates the use of the BIM software REVIT to model a water reservoir for an aqueduct, including structural and plant components, the surrounding context, and proposed construction site organization. The research methodology involves creating a contextualized 3D model to support preliminary safety assessments, work process organization, and the drafting of a safety and coordination plan. Through detailed analysis and critical discussion, this work contributes to understanding how the interaction of regulations and BIM technology can improve construction site safety, offering insights that are applicable beyond the Italian context to the global construction industry. Full article

Agricultural navigation, as an essential part of smart agriculture, is a crucial step in realizing intelligence and, compared with the structured features of urban roads, such as lane-keeping lines, traffic guidance lines, etc., the field environment is more complex. Especially at agricultural intersections, traditional navigation line extraction algorithms make it difficult to achieve the automatic prediction of multiple road navigation lines due to complex unstructured features such as weeds and trees. Therefore, this study proposed a field road navigation line prediction method based on an improved context guided network (CGNet), which can quickly, stably, and accurately detect intersection fields and promptly predict navigation lines for two different directional paths at intersections. Firstly, CGNet will be used to learn the local features of intersections and the joint features of video frames before and after the surrounding environment. Then, the CGNet with a self-attention block module is proposed by adding the self-attention mechanism to improve the semantic segmentation accuracy of CGNet in field road scenes, and the detection speed is not significantly reduced. The semantic segmentation accuracy mIoU is 0.89, and the processing speed is 104 FPS. Subsequently, a field road centerline extraction algorithm is proposed based on the partitioning idea, which can accurately obtain the centerlines of road intersections in the image. The average lateral deviation of each centerline is less than 4%. This study achieved the prediction of intersection navigation lines in mountainous field road scenes, which can provide technical support for field operation road planning of agricultural equipment such as plant protection and harvesting. At the same time, the research findings provide theoretical references for sustainable agricultural development. Full article

This review delves into innovative technologies to improve the control of vascular fungal plant pathogens. It also briefly summarizes traditional biocontrol approaches to manage them, addressing their limitations and emphasizing the need to develop more sustainable and precise solutions. Powerful tools such as next-generation sequencing, meta-omics, and microbiome engineering allow for the targeted manipulation of microbial communities to enhance pathogen suppression. Microbiome-based approaches include the design of synthetic microbial consortia and the transplant of entire or customized soil/plant microbiomes, potentially offering more resilient and adaptable biocontrol strategies. Nanotechnology has also advanced significantly, providing methods for the targeted delivery of biological control agents (BCAs) or compounds derived from them through different nanoparticles (NPs), including bacteriogenic, mycogenic, phytogenic, phycogenic, and debris-derived ones acting as carriers. The use of biodegradable polymeric and non-polymeric eco-friendly NPs, which enable the controlled release of antifungal agents while minimizing environmental impact, is also explored. Furthermore, artificial intelligence and machine learning can revolutionize crop protection through early disease detection, the prediction of disease outbreaks, and precision in BCA treatments. Other technologies such as genome editing, RNA interference (RNAi), and functional peptides can enhance BCA efficacy against pathogenic fungi. Altogether, these technologies provide a comprehensive framework for sustainable and precise management of fungal vascular diseases, redefining pathogen biocontrol in modern agriculture. Full article

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system. Full article