In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots.

Review of grain threshing theory and technology

This paper takes a local drum-type corn thresher as an example. In order to make the threshing principle transform to the plate-tooth type, the width of the spike-tooth threshing component is increased gradually, and three threshing components of different shape and size are selected as the research objects. Based on the preliminary experimental research, the corn threshing process is simulation analyzed using the self-developed corn threshing process analysis software. The effects of the width of the threshing component on the corn ears threshing rate and kernel damage rate under different rates of drum rotation were studied from a macroscopic perspective. The results show that with the increase of drum rotation rate, both the corn ear threshing rate and kernel damage rate increase; with the increase of threshing component width, the threshing rate increases and the damage rate decreases; and when the component width is too large, the stacking between adjacent components has an impact on the threshing performance. The effects of threshing component width on the amount of kernel threshing and the total compressive force during the simulation time were investigated from microscopic perspective at different rates of drum rotation, and the results show that the microscopic analysis is consistent with the macroscopic analysis. Therefore, the optimization of the structural parameters and operating parameters of the threshing component was achieved. When the width of the threshing component was 25 mm and the roller speed was 187.50 rpm, the threshing performance was optimal, with a 98.04% corn ears threshing rate and a 2.56% kernel damage rate. This paper verifies the practical applicability of the corn threshing process analysis software and provides a reference for the optimal design of threshing devices.

Corn was frozen at harvest time in high-latitude areas, when corn kernel is wetter and more easily broken. When frozen corn was threshed and separated by the longitudinal axial threshing cylinder of a combine harvester, it caused a significantly high kernel damage rate and loss rate. The process parameters of threshing cylinder were optimized using RSM (response surface method) and NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). The drum speed (Ds), feed rate (Fr) and concave clearance (Cc) were determined as the optimized process parameters. The loss rate (Lr) and damage rate (Dr) were indicators of operational performance. The RSM was used to establish a mathematical model between process parameters and indicators. With an elite strategy, NSGA-II was used for multi-objective optimization to obtain the optimal operational performance of the threshing cylinder. Overall, when the drum speed was selected as 384.1 rpm, the feed rate as 8.6 kg/s and the concave clearance as 40.5 mm, according to the requirement of corn harvest, the best operational performance of the longitudinal axial threshing cylinder on frozen corn was obtained. The Lr was 1.98% and the Dr was 3.49%. This result indicated that the applicability of the optimal process parameters and the optimization method of combining NSGA-II and RSM was effective for determining the optimal process parameters. This will provide an optimization method for synchronously reducing the loss rate and damage rate of grain harvesters.

Bayesian methods are attracting increasing attention in the field of compressive sensing (CS), as they are applicable to recover signals from random measurements. However, these methods have limited use in many tensor-based cases such as hyperspectral Kronecker compressive sensing (HKCS), because they exploit the sparsity in only one dimension. In this paper, we propose a novel Bayesian model for HKCS in an attempt to overcome the above limitation. The model exploits multi-dimensional block-sparsity such that the information redundancies in all dimensions are eliminated. Laplace prior distributions are employed for sparse coefficients in each dimension, and their coupling is consistent with the multi-dimensional block-sparsity model. Based on the proposed model, we develop a tensor-based Bayesian reconstruction algorithm, which decouples the hyperparameters for each dimension via a low-complexity technique. Experimental results demonstrate that the proposed method is able to provide more accurate reconstruction than existing Bayesian methods at a satisfactory speed. Additionally, the proposed method can not only be used for HKCS, it also has the potential to be extended to other multi-dimensional CS applications and to multi-dimensional block-sparse-based data recovery.