Topographic maps provide detailed information on variations in ground elevation, which is essential for precision farmland levelling. This paper reports the development and experimental study on an innovative approach of generating topographic maps at farmland-level with the advantages of high efficiency and simplicity of implementation. The experiment uses a low-altitude Unmanned Aerial Vehicle (UAV) as a platform and integrates Light Detection and Ranging (LiDAR) distance measurements with Post-Processing Kinematic Global Positioning System (PPK-GNSS) coordinates. A topographic mapping experiment was conducted over two fields in Henan Province, China, and primitive errors of the topographic surveying data were evaluated. The Root Mean Square Error (RMSE) between elevation data of the UAV-LiDAR topographic mapping system and ground truth data was calculated as 4.1 cm and 3.6 cm for Field 1 and Field 2, respectively, which proved the feasibility and high accuracy of the topographic mapping system. Furthermore, the accuracies of topographic maps generated using different geo-spatial interpolation models were also evaluated. The results showed that a TIN (Triangulated Irregular Network) interpolation model expressed the best performances for both Field 1 with sparse topographic surveying points, and Field 2 with relatively dense topographic surveying points, when compared with other interpolation models. Moreover, we concluded that as the spatial resolution of topographic surveying points is intensified from 5 m × 0.5 m to 2.5 m × 0.5 m, the accuracy of the topographic map based on the TIN model improves drastically from 7.7 cm to 4.6 cm. Cut-fill analysis was also implemented based on the topographic maps of the TIN interpolation model. The result indicated that the UAV-LiDAR topographic mapping system could be successfully used to generate topographic maps with high accuracy, which could provide instructive information for precision farmland levelling.

Design and performance of a robotic arm for farm use

In this study, the effects of tillage depth, forward speed and soil moisture content during the cultivator operation on the draft force, energy requirement, and soil disturbance were investigated using five types of cultivators. The experiments were performed in the factorial design based on the randomized complete block design (RCBD) with three replications in loamy sand soil. Different soil moisture contents (factor A) from 5 to 16% for dry soils and 17 to 35% for wet soils, forward speed of tractor (factor B) at four levels of 1.16, 1.61, 1.97, and 3.82 km/hand working depth (factor C) at two levels of 10 and 20 cm were selected. The analysis of variance results showed that the main effects on the draft force, energy requirement, and soil disturbance were significant. With increasing the forward speed, working depth, and blade width, the draft force, energy requirement, and soil disturbance significantly increased. As the soil moisture content increased, the amount of draft force decreased. The average maximum draft force and energy requirement are related to the crescent cultivator and the lowest ones to the cultivator with a sweep blade. The maximum amounts of draft force and energy requirement at the speed of 3.82 km/h were 296.702 N and 0.03708 MJ in the dry conditions, respectively. The average maximum draft force and energy requirement are related to the crescent cultivator and the lowest ones to the cultivator with a sweep blade. The average maximum draft force and energy requirement in dry soil at 10–20 cm depth were 313.534 N and 0.039204 MJ, respectively, and the lowest values were 189 N and 0.019512 MJ in wet soil at the depth of 0–10 cm, respectively. The highest mean value of the area obtained from the profiles was 254.62 cm² related to the dry conditions and forward speed of 3.82 km/h, and the lowest mean value of the area obtained in the wet conditions was 199.6 cm² at the forward speed of 1.16 km/h. The highest average area obtained from the profiles was observed in the dry conditions for C4 as 434.813 cm² and the lowest one was 57.94 cm² in the wet conditions for the cultivator with a chisel blade and L-shaped shank. The highest average area created by cultivators at the 10–20 cm depth in the dry conditions was 332.875 cm² and the lowest one at the 0–10 cm depth in the wet conditions was 123.55 cm². The results of this study can help the designers and manufacturers of agricultural machinery to effectively design and manufacture the machinery with optimum draft and energy requirements.

Robotics can play a significant role to increase efficiency and lighten the farmer’s load. Despite challenges in the agricultural robotic designs, robots are capable of performing various tasks and changing themselves accordingly, based on specific conditions. To address modern problems in the agricultural field, an agricultural robot is one of the key technologies. Although agricultural robotic is still in the development stage, robots have a bright future ahead. This paper proposes a new 5DOF articulated robotic arm design that would become a solution for heavy crop harvestings like pumpkin and cabbage. After the development stage, this robotic arm will be mounted on a robot tractor for real experimentation. The main design process of this robotic arm was conceived using 6 stages of Shigley design process. All components were designed, assembled and analyzed by using Solidworks 2014 in compliance with Japanese Industrial Standards (JIS) standards. The parts of the system that had dynamic nature were analyzed manually using standard mechanical formulas. Calculations of the workspace required joint torque, and coordination of mass center position was done by using standard machine design methods. Denavit-Hartenberg method was used to calculate forward and inverse kinematics. To resolve the torque reduction, components were designed using different materials and mass centers and comparing their performance. Results showed that total torque in Joints number 1, 2, 3, 4 and 5 were 6.15, 257.35, 103.4, 20.2 and 0.1 respectively with a rotational speed range of 15 ~ 60 rpm. Changes in the linkage material and servo motor location improved 29.7% ~ 47.7% and 29.7% ~ 68.9% of the total required torque for each joint. The maximum distance covered by the arm was 1421 mm from the and 2026 mm from the attachment point. According to the feedback received

In this study, artificial neural networks (ANNs) were used to predict the draft force of a rigid tine chisel cultivator. The factorial experiment based on the randomized complete block design (RCBD) was used to obtain the required data and to determine the factors affecting the draft force. The draft force of the chisel cultivator was measured using a three-point hitch dynamometer and data were collected using a DT800 datalogger. A recurrent back-propagation multilayer network was selected to predict the draft force of the cultivator. The gradient descent algorithm with momentum, Levenberg–Marquardt algorithm, and scaled conjugate gradient descent algorithm were used for network training. The tangent sigmoid transfer function was the activation functions in the layers. The draft force was predicted based on the tillage depth, soil moisture content, soil cone index, and forward speed. The results showed that the developed ANNs with two hidden layers (24 and 26 neurons in the first and second layers, respectively) with the use of the scaled conjugate gradient descent algorithm outperformed the networks developed with other algorithms. The average simulation accuracy and the correlation coefficient for the prediction of draft force of a chisel cultivator were 99.83% and 0.9445, respectively. The linear regression model had a much lower accuracy and correlation coefficient for predicting the draft force compared to the ANNs.