Search Results (6)

In this study, the effects of log loading on the lateral overturning of an unmanned, remote-controlled forestry crawler tractor were analyzed through simulation-based analysis. A 3D model was constructed and validated in terms of actual dimensions, static sidelong falling angle, and turning area radius. The errors in both actual dimensions and turning area radius were below 5%, and the static sidelong falling angle was consistent with test results, thereby confirming the model’s reproducibility. Simulations combined three loading levels (0, 50, and 100%), 11 ground slope angles (0 to 50° at 5° intervals), four obstacle heights (0 to 300 mm at 100 mm intervals), and two driving speeds (3.6 and 5.8 km/h). The maximum roll angular velocity within the obstacle contact zone, taken as a safety indicator, was derived for each loading condition. The results showed that lateral overturning occurred before reaching the obstacle, at lower ground slope angles under log loading than without loading. This shows that loading conditions affect lateral discharge safety. Roll angular velocity increased rapidly at high ground slope angles regardless of loading condition, confirming that ground slope angle is key for lateral overturning. Four-way ANOVA results showed that ground slope angle and obstacle height had the greatest impact on roll angular velocity. Although the main effect of loading was relatively small compared to environmental factors, its interaction with ground slope angle was significant, redefining the tractor’s stability limits. Thus, while loading is not a primary factor causing lateral overturning, it influences the sensitivity of roll angular velocity to ground slope angle. These results can be interpreted within a quasi-static framework under low-speed operating conditions, and by using roll angular velocity as an indicator of transient response during obstacle interaction, they provide foundational data for establishing load-dependent safety standards and determining optimal loading limits to prevent lateral overturning in forestry operations. Full article

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article

The development of agricultural farming has evolved from traditional agricultural machinery due to its efficiency and autonomy. Intelligent agricultural machinery is capable of autonomous driving and remote control, but due to its limited perception of farmland and field obstacles, the assistance of unmanned aerial vehicles (UAVs) is required. Although existing intelligent systems have greater advantages than traditional agricultural machinery in improving the quality of operations and reducing labor costs, they also produce complex operational planning problems. Especially as agricultural products and fields become more diversified, it is necessary to develop an adaptive operation planning method that takes into account the efficiency and quality of work. However, the existing operation planning methods lack practicality and do not guarantee global optimization because traditional planners only consider the path commands and generate the path in the rectangular field without considering other factors. To overcome these drawbacks, this paper proposes a novel and practical collaborative path planning method for intelligent agricultural machinery based on unmanned aerial vehicles. First, we utilize UAVs for obstacle detection. With the field information and operation data preprocessed, automatic agricultural machinery could be assisted in avoiding obstacles in the field. Second, by considering both the historical state of the current operation and the statistics from previous operations, the real-time control of agricultural machinery is determined. Therefore, the K-means algorithm is used to extract key control parameters and discretize the state space of agricultural machinery. Finally, the dynamic operation plan is established based on the Markov chain. This plan can estimate the probability of agricultural machinery transitioning from one state to another by analyzing data, thereby dynamically determining real-time control strategies. The field test with an automatic tractor shows that the operation planner can achieve higher performance than the other two popular methods. Full article

The need for high-quality communications networks is urgent in data-based farming. A particular challenge is how to achieve reliable, cost-efficient, secure, and broadband last-mile data transfer to enable agricultural machine control. The trialed ad hoc private communications networks built and interconnected with different alternative wireless technologies, including 4G, 5G, satellite and tactical networks, provide interesting practical solutions for connectivity. A remotely controlled tractor is exemplified as a use case of machine control in the demonstrated private communication network. This paper describes the results of a comparative technology analysis and a field trial in a realistic environment. The study includes the practical implementation of video monitoring and the optimization of the control channel for remote-controlled unmanned agricultural tractors. The findings from this study verify and consolidate the requirements for network technologies and for cybersecurity enablers. They highlight insights into the suitability of different wireless technologies for smart farming and tractor scenarios and identify potential paths for future research. Full article

With rapidly advancing technologies such as IoT, AI, robotics, and others, smart agriculture in Japan has been introduced and tested throughout the country. The validity of the implementation of smart agriculture could be measured by using cost analysis, working capacity assessment, and management efficiency analysis. In this study, we focused on pest-control management, wherein unmanned aerial vehicles (UAVs) for crop spraying have been recently introduced. In order to clarify the validity of UAVs for rice fields in Japan regarding costs and performance, we conducted a comparative study of pest-control sprayers, specifically: (1) tractor- mounted boom sprayers, (2) remote-control spraying helicopters (RC helicopters), and (3) UAVs. We estimated pest-control costs and the working capacity of each method. We also evaluated the management efficiency of 21 case scenarios of different pest-control sprayers and field areas ranging from 0.5 to 30 ha using data envelopment analysis (DEA) based on an input-oriented model. We used the input of pest-control cost and the output of gross farm income and surplus working capacity. Pest-control costs per unit area of boom sprayers, RC helicopters, and UAVs were approximately 925,597 yen/ha (US $8819/ha), 6,924,455 yen/ha (US $65,975/ha), and 791,724 yen/ha (US $7543/ha), respectively. The working capacity during pest-control scheduled days was 120, 195, and 135 ha, respectively. DEA results suggested that UAVs would be more efficient than boom sprayers and RC helicopters for the analyzed cases. UAVs for crop spraying showed relatively low cost and high management efficiency compared to the boom sprayers and RC helicopters; hence UAVs could be a suitable replacement to save cost and time. Full article

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations. Full article