Review of the field environmental sensing methods based on multi-sensor information fusion technology

Aiming to ease the cutting of kenaf stalks via bionics, the bionic disc cutter was designed. The upper jaw of the Batocera horsfieldi was used as a bionic prototype. Further, to explore its dynamic performance, an indoor multi-stalk cutting experimental bench was used to simulate the field operation process. A three-factor and two-level interaction orthogonal experiment was carried out; cutting speed, stalk conveying speed (machine forward speed), and blade type (ordinary disc cutter and bionic disc cutter) were used as experiment factors. The cutting pass rate and cutting specific energy consumption were selected as evaluation indexes. The range, variance, and fuzzy comprehensive evaluation analysis were carried out on the experiment results. Moreover, the main order factors affecting the bionic cutter performance were determined-blade type, conveying speed, the interaction between the cutter speed and conveying speed, and cutter speed. The optimal parameter combination scheme had a cutter speed of 1000 r/min, conveying speed of 0.4 m/s, and included bionic blades. Under this condition, the best index was the 92% cutting pass rate, with a specific energy consumption of 4.38 J/stalk. The variance analysis has shown that, with 95% confidence, the blade type has a rather significant influence on the comprehensive index. Additionally, the conveying speed also significantly influenced it, while other factors and interactions had no notable effect. The experimental comparison under the same working condition shows that the bionic blade has better cutting effect. This study provides a reference for the development of cutter and the selection of kenaf harvester operational parameters. Keywords: kenaf harvester, bionic, disc cutter, orthogonal experiment, interaction DOI: 10.25165/j.ijabe.20231606.7997 Citation: Tian K P, Zhang B, Ji A M, Huang J C, Liu H L, Shen C. Design and experiment of the bionic disc cutter for kenaf harvesters. Int J Agric & Biol Eng, 2023; 16(6): 116–123.

Aiming at the problem of large differences in the laying angle and posture of plants cut by the hemp harvester, which is unfavorable for the subsequent picking-up, this paper analyzed the laying process and laying angles, and built a conveyor-plant rigid-flexible coupling model for simulating the laying of hemp plant. Moreover, the operating parameters were tested and optimized based on the central composite design theory, and carried out multi-objective optimization with the minimum laying angle as the response index. Firstly, the formation mechanism of the laying angle of hemp harvester was studied. Secondly, a test was designed with the quadratic orthogonal rotational combination test method, with the data being processed by Design-Expert. A regression mathematical model of the laying angle was built, and the influence of the interactions between factors on the laying angle was analyzed with the response surface method. Furthermore, multi-objective optimization was conducted on the regression model according to the actual production design requirements. As a result, the best combination was obtained, that is, when the forward speed is 0.7 m/s, speed ratio 1.40, and stubble height 95 mm, the minimum laying angle can be obtained, namely 124.9°. The optimization parameters were verified by the simulation and field tests. The simulation test showed that the simulated laying angle is 125.2°, with a relative error of 0.24% from the theoretical value, under the best combination of parameters. The field test showed that the average laying angle of hemp plant is 121.8°, with a relative error of 2.5% from the theoretical value, under the best combination of parameters. The results may provide a reference for the structural improvement and operating parameter control of hemp harvesters. Keywords: agricultural machinery, hemp, laying angle, rigid-flexible coupling model, optimization, response surface analysis DOI: 10.25165/j.ijabe.20231604.7978 Citation: Huang J C, Tan L, Tian K P, Zhang B, Ji A M, Liu H L, Shen C. Formation mechanism for the laying angle of hemp harvester based on ANSYS-ADAMS. Int J Agric & Biol Eng, 2023; 16(4): 109–115.

When uncrewed agricultural machinery performs autonomous operations in the field, it inevitably encounters obstacles such as persons, livestock, poles, and stones. Therefore, accurate recognition of obstacles in the field environment is an essential function. To ensure the safety and enhance the operational efficiency of autonomous farming equipment, this study proposes an improved YOLOv8-based field obstacle detection model, leveraging depth information obtained from binocular cameras for precise obstacle localization. The improved model incorporates the Large Separable Kernel Attention (LSKA) module to enhance the extraction of field obstacle features. Additionally, the use of a Poly Kernel Inception (PKI) Block reduces model size while improving obstacle detection across various scales. An auxiliary detection head is also added to improve accuracy. Combining the improved model with binocular cameras allows for the detection of obstacles and their three-dimensional coordinates. Experimental results demonstrate that the improved model achieves a mean average precision (mAP) of 91.8%, representing a 3.4% improvement over the original model, while reducing floating-point operations to 7.9 G (Giga). The improved model exhibits significant advantages compared to other algorithms. In localization accuracy tests, the maximum average error and relative error in the 2–10 m range for the distance between the camera and five types of obstacles were 0.16 m and 2.26%. These findings confirm that the designed model meets the requirements for obstacle detection and localization in field environments.

In response to the unclear issue of whether the dynamic cutting performance and structural parameters of an industrial hemp blade, which was developed earlier based on the bionic prototype of the batocera horsfieldi, can be optimized in actual working conditions, this paper analyzes the effective clamping conditions of a reciprocating double-acting cutting blade for stalks and the cutting motion. To investigate the effect of different structural and motion parameters, as well as their interactions, of the bionic blade on cutting energy consumption, bionic blades with different combinations of tooth pitch and tooth angle were designed. A Box–Behnken response surface method with three factors and three levels was used to design an experimental scheme. Utilizing rigid-flexible coupling numerical simulation technology, numerical simulation experiments were conducted to investigate the cutting performance of industrial hemp stalks using the blade. A regression model for cutting energy consumption was established, and variance analysis indicated that tooth angle, speed ratio, and the interaction between tooth angle and speed ratio had an extremely significant effect on the regression model. The primary and secondary orders of factors affecting cutting energy consumption were determined to be: speed ratio > tooth angle > tooth pitch. Through optimization, the optimal parameter combination was found to be a blade tooth pitch of 6.61 mm, a tooth angle of 30°, and a speed ratio of 1.62. Under these conditions, the cutting energy consumption was 3947.99 mJ. The optimized parameters were verified through numerical simulation cutting experiments, and the results showed that the error compared with the optimization results was only 8.16%. This indicates that the optimization results have high credibility and further verifies the reliability of the model. This study can provide a reference for the development of cutting devices for industrial hemp harvesters and the selection of motion parameters.

Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned using LiDAR to obtain point cloud data, which are then subjected to pass-through filtering and statistical filtering to remove noise and non-corn contour points. Subsequently, Euclidean clustering and K-means clustering methods are applied to the filtered point cloud data. To validate the impact of Euclidean clustering on subsequent clustering, two separate treatments of the obtained point cloud data were conducted during experimental validation: the first used the K-means clustering algorithm directly, while the second involved performing Euclidean clustering followed by K-means clustering. The results demonstrate that the combined method of Euclidean clustering and K-means clustering achieved a success rate of 81.5%, representing a 26.5% improvement over traditional K-means clustering. Additionally, the Rand index increased by 0.575, while accuracy improved by 57% and recall increased by 61%.