Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Conventional redesign methodologies applied on the grain harvester headers for the mechanical harvesting of chickpeas cause its progress to not be as rapid and technological. This paper presents a hybrid modeling-optimization methodology to design harvester reels for efficient chickpea harvesting. The five fabricated headers were tested in both real and virtual modeling environments to optimize the operational parameters of the reel for minimum losses. Harvesting losses data gathered from chickpea fields over ten years of trials were fed into a fuzzy logic model, which in turn was merged with simulated annealing to develop a simulator. To this end, simulated annealing was used to produce combinations of reel diameter and number of bats, to be fed into the fuzzy model until achieving a minimum harvesting loss. The proposed model predicts the reel structure measured in-field evaluation, which fits well with the previously established mathematical model. A significant improvement in harvesting performance, 71% pod harvesting, validates the benefits of the proposed fuzzy-simulated annealing approach to optimize the design of grain harvester headers

Modeling the optimal factors affecting combine harvester header losses

Combine header loss comprises more than 50% of wheat harvesting losses.  Therefore, decline in this part of the loss to the extent allowed amount is an important step in reducing of crop wastes.  Combine header is a complex system in which several factors are involved in its work.  And, if these factors can be adjusted and controlled to suit the working conditions, to a large extent of crop loss can be prevented during the harvest.  In this study, reel index, cutting height of crop and horizontal and vertical distance of reel from cutter bar were selected as the effective factors in header loss.  In response surface method, central composite design was used to modeling and finding optimal levels of mentioned factors.  The results showed that power model was the best model to describe the dependence of the independent variables and the dependent variable.  The optimum conditions for minimum combine header loss (103 kg/ha) were obtained 1.2, 25 and 5 for reel index, cutting height of crop and horizontal and vertical distances of reel from cutter bar, respectively

Optimization of combine processes using expert knowledge and methods of artificial intelligence

Combine harvesters are used to gather plants from the field and separate them into the components of value, the grain and the straw. The optimal utilization of existing combine potential is an inevitable task to maximize harvest efficiency and hence to maximize profit. The only way to optimize the threshing and separation processes during harvest is to adjust the combine settings to existing conditions. Operating permanently at optimal harvest efficiency can only be achieved by an automatic control system. However, for reasons of transparency and due to lack of sensors, the approach in this thesis is a combined development of an interactive and an automatic control system for combine process optimization. The optimization of combine processes is a multi-dimensional and multi-objective optimization problem. The objectives of optimization are the harvest quality parameters. The decision variables, the parameters that can be modified, are the combine settings. Analytical optimization methods require the existence of a model that provides function values in dependence of defined input parameters. A comprehensive quantitative model for the input-output-behavior of the combine does not exist. Alternative optimization methods that handle multi-dimensional and multi-objective optimization problems can be found in the domain of Artificial Intelligence. In this work, knowledge acquisition was performed in order to obtain expert knowledge on combine process optimization. The result is a knowledge base with six adjustment matrices for different crop and combine types. The adjustment matrices contain problem oriented setting adjustment recommendations in order to solve single issues with quality parameters. A control algorithm has been developed that is also capable of solving multiple issues at the same time, utilizing the acquired expert knowledge. The basic principle to solve the given multi-objective optimization problem is a transformation into one-dimensional single-objective optimization problems which are solved iteratively. Several methods have been developed that are applied sequentially. In simulation, the average improvement from initial settings to optimized settings, achieved by the control algorithm, is between 34.5 % and 67.6 %. This demonstrates the good performance of the control algorithm

Optimization of Millet Axial Flow Threshing and Separation Device Based on Discrete Element Method

The difficulties of threshing and separation of millet have not been solved yet which has restricted the development of the millet industry because of the special biological structure and lack of professional agricultural machinery. In order to improve the quality of millet harvest and meet the market demand for millet, in this paper, according to the branching structure of millet, the millet earhead model was established by Discrete Element Method. Using virtual models of millet and device, the simulation tests were carried out whose results have shown that the threshing effect of the rasp-bar threshing element is better than that of the teeth threshing element. Then the rotor structure was optimized into a combined type of the rasp-bar and the teeth. A three-factor five-level quadratic orthogonal rotation combination test was carried out whose results have shown that the combined rotor can meet the requirements of millet harvest

Agricultural Structures and Mechanization

In our globalized world, the need to produce quality and safe food has increased exponentially in recent decades to meet the growing demands of the world population. This expectation is being met by acting at multiple levels, but mainly through the introduction of new technologies in the agricultural and agri-food sectors. In this context, agricultural, livestock, agro-industrial buildings, and agrarian infrastructure are being built on the basis of a sophisticated design that integrates environmental, landscape, and occupational safety, new construction materials, new facilities, and mechanization with state-of-the-art automatic systems, using calculation models and computer programs. It is necessary to promote research and dissemination of results in the field of mechanization and agricultural structures, specifically with regard to farm building and rural landscape, land and water use and environment, power and machinery, information systems and precision farming, processing and post-harvest technology and logistics, energy and non-food production technology, systems engineering and management, and fruit and vegetable cultivation systems. This Special Issue focuses on the role that mechanization and agricultural structures play in the production of high-quality food and continuously over time. For this reason, it publishes highly interdisciplinary quality studies from disparate research fields including agriculture, engineering design, calculation and modeling, landscaping, environmentalism, and even ergonomics and occupational risk prevention

Formalization of Fuzzy Statements in the Task of Technological Adjustment of Grain Combines

The article considers various types of statements used in the linguistic description of input factors and regulated parameters of a combine harvester when solving the problem of its technological adjustment. The linguistic description of the environmental conditions, the regulated parameters of the combine and the characteristics of the quality of harvesting operations is presented. The technique of modeling fuzzy statements for the formalization of knowledge in an expert system designed to inform the operator of the combine when choosing the values of adjustable parameters corresponding to the operating conditions of the combine is presented. The technique is illustrated by an example of fuzzy inference

Research of the process of air separation of grain material in a vertical zigzag channel

The aim of the research is to determine ways to improve the efficiency of grain materials pneumatic separation based on the study of the interaction mechanism of grains with the air flow in the channel elements, which are inclined sections of the air duct. The paper considers a modified separation method that separates the grain material according to the aerodynamic properties of the components in a vertical zigzag air channel in an aerodynamic and gravitational field. The studies were carried out by means of mathematical modeling followed by experimental confirmation of the modeling results. A computer mathematical model was created in the MathCad 13 application package to determine the parameters of the zigzag aspiration channel. This model describes the influx of parameters in the channel (width, height, cut of the zigzag ledge to the horizon, edge of the zigzag ledge) on the nature of the grain trajectory. As a result of the numerical simulation process using the mathematical package Mathematica, dependence was obtained to determine the trajectory of motion depending on the angular velocity. An increase in the pneumatic separation effect during the grain mixtures division due to the influence of the Magnus effect was established as a result of experimental studies. The design of a zigzag separator with an annular channel section has been improved based of the research carried out