Search Results (16)

LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, plant spacing, and intra-row spacing measurements) and the recognition of agricultural working environments (e.g., field boundaries, ridges, and obstacles) using agricultural field machinery. The objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments. This study also highlights LiDAR sensor testing procedures, focusing on critical parameters, industry standards, and accuracy benchmarks; it evaluates the specifications of various commercially available LiDAR sensors with applications for plant feature characterization and highlights the importance of mounting LiDAR technology on agricultural machinery for effective recognition of crops and working environments. Different studies have shown promising results of crop feature characterization using an airborne LiDAR, such as coefficient of determination (R²) and root-mean-square error (RMSE) values of 0.97 and 0.05 m for wheat, 0.88 and 5.2 cm for sugar beet, and 0.50 and 12 cm for potato plant height estimation, respectively. A relative error of 11.83% was observed between sensor and manual measurements, with the highest distribution correlation at 0.675 and an average relative error of 5.14% during soybean canopy estimation using LiDAR. An object detection accuracy of 100% was found for plant identification using three LiDAR scanning methods: center of the cluster, lowest point, and stem–ground intersection. LiDAR was also shown to effectively detect ridges, field boundaries, and obstacles, which is necessary for precision agriculture and autonomous agricultural machinery navigation. Future directions for LiDAR applications in agriculture emphasize the need for continuous advancements in sensor technology, along with the integration of complementary systems and algorithms, such as machine learning, to improve performance and accuracy in agricultural field applications. A strategic framework for implementing LiDAR technology in agriculture includes recommendations for precise testing, solutions for current limitations, and guidance on integrating LiDAR with other technologies to enhance digital agriculture. Full article

Agricultural machinery services are an important guaranteed way to promote Conservation Agriculture. It is of great significance to study how to encourage farmers to choose agricultural machinery services to promote the standard implementation of Conservation Agriculture technology. In order to promote the implementation of Conservation Agriculture and improve the supply of agricultural machinery services, this paper identifies the stakeholders of normative Conservation Agriculture technology adoption behavior and the relationship between agricultural machinery service organizations, farmers and agriculture-related governments. An evolutionary game model was established to evaluate the decision-making characteristics of tripartite behavior and simulate the evolution trend of stakeholder behavior. The results show that agriculture-related governments, agricultural machinery service organizations and farmers can achieve evolutionarily stable strategies. The punishments and subsidies of agriculture-related governments and the supervision cost of all links of agricultural machinery social service organizations can significantly affect the behavior strategies of the three parties. The government set up reasonable subsidy and punishment mechanisms, and the agricultural machinery service organization controls the supervision cost of all links to ensure the stability of the three-party behavior strategy. This study provides theoretical guidance for scientific decision making and active cooperative development of the government, farmers and agricultural machinery service organizations and lays a foundation for countermeasures and suggestions to further promote farmers’ implementation of Conservation Agriculture technology. Full article

Pest control is an important guarantee for agricultural production. Pests are mostly light-avoiding and often gather on the bottom of crop leaves. However, spraying agricultural machinery mostly adopts top-down spraying, which suffers from low pesticide utilization and poor insect removal effect. Therefore, the upward spraying mode and intelligent nozzle have gradually become the research hotspot of precision agriculture. This paper designs a leaf-bottom pest control robot with adaptive chassis and adjustable selective nozzle. Firstly, the adaptive chassis is designed based on the MacPherson suspension, which uses shock absorption to drive the track to swing within a 30° angle. Secondly, a new type of cone angle adjustable selective nozzle was developed, which achieves adaptive selective precision spraying under visual guidance. Then, based on a convolutional block attention module (CBAM), the multi-CBAM-YOLOv5s network model was improved to achieve a 70% recognition rate of leaf-bottom spotted bad point in video streams. Finally, functional tests of the adaptive chassis and the adjustable selective spraying system were conducted. The data indicate that the adaptive chassis can adapt to diverse single-ridge requirements of soybeans and corn while protecting the ridge slopes. The selective spraying system achieves 70% precision in pesticide application, greatly reducing the use of pesticides. The scheme explores a ridge-friendly leaf-bottom pest control plan, providing a technical reference for improving spraying effect, reducing pesticide usage, and mitigating environmental pollution. Full article

The precision of agro-technical operations is one of the main hallmarks of a modern approach to agriculture. However, ensuring the precise application of plant protection products or the performance of mechanical field operations entails significant costs for sophisticated positioning systems. This paper explores the integration of precision positioning based on the global navigation satellite system (GNSS) in agriculture, particularly in fieldwork operations, seeking solutions of moderate cost with sufficient precision. This study examines the impact of GNSSs on automation and robotisation in agriculture, with a focus on intelligent agricultural guidance. It also discusses commercial devices that enable the automatic guidance of self-propelled machinery and the benefits that they provide. This paper investigates GNSS-based precision localisation devices under real field conditions. A comparison of commercial and low-cost GNSS solutions, along with the integration of satellite navigation with advanced visual odometry for improved positioning accuracy, is presented. The research demonstrates that affordable solutions based on the common differential GNSS infrastructure can be applied for accurate localisation under real field conditions. It also underscores the potential of GNSS-based automation and robotisation in transforming agriculture into a more efficient and sustainable industry. Full article

Repeated and missed spraying are common problems during the working of boom sprayers, especially in the breakpoint continuous process. Therefore, the present study investigated a breakpoint continuous spraying system for automatic-guidance boom sprayers based on a hysteresis compensation algorithm for spraying. An operational breakpoint identification algorithm, which combines a real-time kinematic global navigation satellite system (RTK-GNSS) and wheel odometer, was proposed; a pre-adjusted proportional-integral-derivative (PID) control algorithm for the opening degree of the proportional control valve was designed in thus study. Tests were conducted to establish equations correlating the opening degree of the proportional control valve, pump output flow rate, and main pipeline flow rate, with an R² ≥ 0.9525. The time to adjust to the target flow rate was experimentally tested. The breakpoint identification accuracy of the RTK-GNSS and RTK-GNSS + wheel odometer was experimentally assessed. A field spraying deposition variation experiment was conducted. According to the results, the system effectively eliminated missed spraying, with a maximum repeated spraying distance of ≤3.3 m, and it achieved a flow control error within 3%. This system also reduced the repeated spraying area and enhanced the pesticide spraying quality of breakpoint continuous spraying for automatic-guidance boom sprayers. Full article

Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information monitoring, which promotes the transformation of the intelligent computing of remote sensing big data and agricultural intensive management from theory to practical applications. In this paper, the main research objective is to construct a new high-frequency agricultural production monitoring and intensive sharing service and management mode, based on the three dimensions of space, time, and attributes, that includes crop recognition, growth monitoring, yield estimation, crop disease or pest monitoring, variable-rate prescription, agricultural machinery operation, and other automatic agricultural intelligent computing applications. The platforms supported by this mode include a data management and agricultural information production subsystem, an agricultural monitoring and macro-management subsystem (province and county scales), and two mobile terminal applications (APPs). Taking Shandong as the study area of the application case, the technical framework of the system and its mobile terminals were systematically elaborated at the province and county levels, which represented macro-management and precise control of agricultural production, respectively. The automatic intelligent computing mode of satellite–air–ground spatiotemporal collaboration that we proposed fully couples data obtained from satellites, unmanned aerial vehicles (UAVs), and IoT technologies, which can provide the accurate and timely monitoring of agricultural conditions and real-time guidance for agricultural machinery scheduling throughout the entire process of agricultural cultivation, planting, management, and harvest; the area accuracy of all obtained agricultural information products is above 90%. This paper demonstrates the necessity of customizable product and service research in agricultural intelligent computing, and the proposed practical mode can provide support for governments to participate in agricultural macro-management and decision making, which is of great significance for smart farming development and food security. Full article

Promoting farmers’ participation in rural industrial integration and driving farmers’ agricultural production with cooperatives and agribusinesses are conducive to realizing cost saving, efficiency, and green production and guaranteeing food security and sustainable agricultural development. Based on the microsurvey data of 1039 grain farmers in Henan Province, China in 2022, this paper examined the impact of contractual choices of farmers’ participation in rural industrial integration on agricultural green productivity while analyzing the mechanism of action by using OLS regression, a causal mediation analysis of instrumental variables, propensity score matching, and two-stage least squares (2SLS). The study found that: (1) farmers’ participation in a contract, driven by cooperatives or agribusinesses to carry out agricultural production, is conducive to improving their agricultural green productivity, but the effect of each main body to drive farmers varies; (2) farmers’ participation in a contract, through cooperatives or agribusinesses to obtain all kinds of agricultural production services—such as agricultural machinery services, agricultural supply services, and technical guidance services—improves the use of agricultural machinery, the standardization of chemical fertilizers, pesticides, and other agricultural materials’ use, increases technical guidance, and improves agricultural green productivity. The findings of this paper suggest policy and practical implications for safeguarding food security and promoting sustainable agriculture, as well as enriching research on agricultural productivity. Full article

Global Navigation Satellite Systems (GNSS) in precision agriculture (PA) represent a cornerstone for field mapping, machinery guidance, and variable rate technology. However, recent improvements in GNSS components (GPS, GLONASS, Galileo, and BeiDou) and novel remote sensing and computer processing-based solutions in PA have not been comprehensively analyzed in scientific reviews. Therefore, this study aims to explore novelties in GNSS components with an interest in PA based on the analysis of scientific papers indexed in the Web of Science Core Collection (WoSCC). The novel solutions in PA using GNSS were determined and ranked based on the citation topic micro criteria in the WoSCC. The most represented citation topics micro based on remote sensing were “NDVI”, “LiDAR”, “Harvesting robot”, and “Unmanned aerial vehicles” while the computer processing-based novelties included “Geostatistics”, “Precise point positioning”, “Simultaneous localization and mapping”, “Internet of things”, and “Deep learning”. Precise point positioning, simultaneous localization and mapping, and geostatistics were the topics that most directly relied on GNSS in 93.6%, 60.0%, and 44.7% of the studies indexed in the WoSCC, respectively. Meanwhile, harvesting robot research has grown rapidly in the past few years and includes several state-of-the-art sensors, which can be expected to improve further in the near future. Full article

Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported. Full article

We propose a Semantic Feature Pyramid Network (FPN)-based algorithm to derive agricultural field boundaries and internal non-planting regions from satellite imagery. It is aimed at providing guidance not only for land use management, but more importantly for harvest or crop protection machinery planning. The Semantic Convolutional Neural Network (CNN) FPN is first employed for pixel-wise classification on each remote sensing image, detecting agricultural parcels; a post-processing method is then developed to transfer attained pixel classification results into closed contours, as field boundaries and internal non-planting regions, including slender paths (walking or water) and obstacles (trees or electronic poles). Three study sites with different plot sizes (0.11 ha, 1.39 ha, and 2.24 ha) are selected to validate the effectiveness of our algorithm, and the performance compared with other semantic CNN (including U-Net, U-Net++, PSP-Net, and Link-Net)-based algorithms. The test results show that the crop acreage information, field boundaries, and internal non-planting area could be determined by using the proposed algorithm in different places. When the boundary number applicable for machinery planning is attained, average and total crop planting area values all remain closer to the reference ones generally when using the semantic FPN with post-processing, compared with other methods. The post-processing methodology would greatly decrease the number of inapplicable and redundant field boundaries for path planning using different CNN models. In addition, the crop planting mode and scale (especially the small-scale planting and small/blurred gap between fields) both make a great difference to the boundary delineation and crop acreage determination. Full article

Agricultural machinery can improve agricultural productivity and promote agricultural scale operation. However, machinery operations lead to increased dust in farmland and affect the atmospheric environment; thus, they have been increasingly emphasized. In this study, the effects of agricultural machinery operations in wheat cultivation were investigated regarding the emissions of three kinds of particulate matters, namely fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀) and total suspended particulate (TSP), from farmland in Beijing. The results showed that the total dust emission from the traditional tillage mode, including straw crushing, rotary tilling and sowing, was 3.990 g per hectare, which was larger than that of the conservation tillage mode including only no-tillage sowing (0.407 g per hectare). The total dust emission for one hectare of farmland under the two modes was 3.415 g, 0.497 g, 0.407 g and 0.078 g for straw shredding, rotary tillage, no-tillage sowing and conventional sowing, respectively. The values of PM_2.5/PM₁₀ and PM_2.5/TSP decreased in each tillage section after each agricultural machinery operation, while the values of PM₁₀/TSP were basically unchanged, indicating that particulate matter emissions from farmland due to agricultural machinery operations are mainly PM₁₀ and TSP. The dust concentration generated by agricultural machinery increased with an increase in the speed of the machinery operation, provided that the quality of the operation was guaranteed. This study provides guidance for reducing dust emissions from mechanized operations, improving air quality and decreasing health hazards to operators of agricultural machinery. Full article

Farmland environmental pollution has put greater pressure on the sustainability of agricultural production systems. Exploring the relationship between farm size and environmental pollution in agriculture can help provide realistic guidance for stakeholders. In this study, the research data from apple farmers in China were used to measure the environmental pollutant emissions caused by apple production using the life-cycle assessment (LCA) approach. The mediating effect model was used to examine the mechanisms and pathways by which farm size affects the environmental effects of apple production and to identify the mediating effects of fertilizer, pesticide, and machinery input intensity. Finally, a heterogeneity analysis was conducted to illustrate the impact of participation in agricultural cooperatives on the environmental performance of apple production for smallholder farmers. The results showed that the apple production system’s negative environmental impacts from the agricultural material production phase were more significant compared to the farming phase, with a contribution potential of 56.50%. Farm size directly impacts the environmental effects of apple production, and there is a U-shaped trend between the two, implying that from the perspective of environmental effects, larger farm size is not better. There were some mediating effects in the paths of farm size on the environmental effects, and the largest effect was fertilizer input intensity with a full mediating effect; the second largest effect was machinery input intensity with a partial mediating effect, and the mediating effect accounted for 15.50–15.89% of the total effect; the mediating effect of pesticide input intensity was not significant. In addition, the study also found that joining agricultural cooperatives was beneficial in promoting the improvement of the negative environmental impact caused by apple production. These findings provide insights into optimizing farm inputs for apple production and identifying the appropriate farm size to alleviate multiple environmental impacts, intending to make a marginal contribution to promoting sustainable development of the apple industry in China also providing the research evidence for the comparative study of the environmental burdens of apple production in China and other countries in the world. Full article

Agricultural machinery telemetry collects and shares data, which are sent remotely and become precious information. Thus, accurate and instantaneous monitoring can provide an important base of information for adjusting the parameters of the most diverse mechanized agricultural operations, reducing input costs and maintenance expenses. In recent years, this theme has gained more strength and importance for managing rural properties. Therefore, the present study developed a bipartite bibliometric analysis in two lines of research and described the state of the art of this data collection methodology (via telemetry), presenting its technological evolution. The study presents the evolution and connection of telemetry and the processes of robotization of agricultural operations and automation provided by data collection via telemetry in real time. The main countries, keywords, researchers, institutions, and the Dickson quality index indicate a high growth in the last decade. Thus, the present study contributes to decision making regarding research topics, guidance on the state of the art, and contextualization of telemetry’s importance in current research. Full article

In order to design a branch-cutting type green pepper harvesting device, we firstly study the whole process of straight knife green pepper cutting to reveal the cutting mechanism and provide theoretical guidance to the design. A finite element model was established for the cutting of pepper branches across the distance, and single-factor and multi-factor finite element simulation tests were conducted on the knife feed angle, tool edge angle, and knife feed speed of the working parts of the pepper cutting and harvesting device. The results of the experiment were analyzed by ANOVA, which showed the different degrees of importance of these factors, and the optimal parameters were obtained by response surface methodology (RSM). With the optimal parameters selected, the predicted maximum cutting force and cutting completion were 803.35 N and 98.58%, respectively, this satisfies the efficiency and economy requirements of agricultural machinery design. In addition, the cutting force of green pepper branches was analyzed and a theoretical mechanical model was developed to help us understand the variation of cutting force numerically. The stress–strain system, high-speed photography system and numerical prediction were innovatively combined to observe and measure the stress and other key state variables in the cutting process in detail, summarize their changing trend, and establish a time-based monitoring and comparison model. The above research results can provide a reference for the design of green pepper branch cutting and harvesting devices, such as direct guidance on the selection of working parameters, materials, etc., and guidance on the operation in actual work. Full article

Young adults enrolled in collegiate agricultural programs are a critical audience for agricultural health and safety training. Understanding the farm tasks that young adults engage in is necessary for tailoring health and safety education. The project analyzed evaluation survey responses from the Gear Up for Ag Health and Safety™ program, including reported agricultural tasks, safety concerns, frequency of discussing health and safety concerns with healthcare providers, safety behaviors, and future career plans. The most common tasks reported included operation of machinery and grain-handling. Most participants intended to work on a family-owned agricultural operation or for an agribusiness/cooperative following graduation. Reported safety behaviors (hearing protection, eye protection, and sunscreen use when performing outdoor tasks) differed by gender and education type. Male community college and university participants reported higher rates of “near-misses” and crashes when operating equipment on the roadway. One-third of participants reported discussing agricultural health and safety issues with their medical provider, while 72% were concerned about the health and safety of their family and co-workers in agriculture. These findings provide guidance for better development of agricultural health and safety programs addressing this population—future trainings should be uniquely tailored, accounting for gender and educational differences. Full article