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

Existing corn harvester cutting blades have problems associated with large cutting resistance, high energy consumption, and poor cut quality. Using bionics principles, a bionic blade was designed by extracting the cutting tooth profile curve of the B. horsfieldi palate. Using a double-blade cutting device testing system, a single stalk cutting performance contrast test for corn stalks obtained at harvest time was carried out. Results show that bionic blades have superior performance, demonstrated by strong cutting ability and good cut quality. Using statistical analysis of two groups of cutting test data, the average cutting force and cutting energy of bionic blades and ordinary blades were obtained as 480.24 N and 551.31 N and 3.91 J and 4.38 J, respectively. Average maximum cutting force and cutting energy consumption for the bionic blade were reduced by 12.89% and 10.73%, respectively. Variance analysis showed that both blade types had a significant effect on maximum cutting energy and cutting energy required to cut a corn stalk. This demonstrates that bionic blades have better cutting force and energy consumption reduction performance than ordinary blades.

Influence of vibration on the grain flow sensor during the harvest and the difference elimination method

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.

Environment perception is a basic and necessary technology for autonomous vehicles to ensure safety and reliable driving. A lot of studies have focused on the ideal environment, while much less work has been done on the perception of low-observable targets, features of which may not be obvious in a complex environment. However, it is inevitable for autonomous vehicles to drive in environmental conditions such as rain, snow and night-time, during which the features of the targets are not obvious and detection models trained by images with significant features fail to detect low-observable target. This article mainly studies the efficient and intelligent recognition algorithm of low-observable targets in complex environments, focuses on the development of engineering method to dual-modal image (color–infrared images) low-observable target recognition and explores the applications of infrared imaging and color imaging for an intelligent perception system in autonomous vehicles. A dual-modal deep neural network is established to fuse the color and infrared images and detect low-observable targets in dual-modal images. A manually labeled color–infrared image dataset of low-observable targets is built. The deep learning neural network is trained to optimize internal parameters to make the system capable for both pedestrians and vehicle recognition in complex environments. The experimental results indicate that the dual-modal deep neural network has a better performance on the low-observable target detection and recognition in complex environments than traditional methods.

Design and parameters optimization for cutting-conveying mechanism of ramie combine harvester