Mathematical model-based redesign of chickpea harvester reel

Golpira, Hiwa; Rovira-Más, Francisco; Golpîra, Hêmin; Sáiz-Rubio, Verónica

doi:10.5424/sjar/2021191-16391

Cited by 3 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is noteworthy that, the physical and mechanical properties of the crop, the plant varieties, height, and distances in rows are hidden in the field gathering data. These results were confirmed by [40] both in kinematic analysis and field experiments results. Finally, the optimized reel was assembled on the redesigned prototype chickpea harvester model 2021 as shown in Figure 7.…”

Section: Defuzzificationsupporting

confidence: 75%

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Golpira

Sola-Guirado

2022

Agriculture

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Defuzzificationsupporting

confidence: 75%

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Golpira

Sola-Guirado

2022

Agriculture

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the elementary theory of elastic bending theory [24] , at the point of the deflected cantilever, which could be described as: (11) where, EI denotes flexural stiffness, N•m 2 ; F denotes external force on deflecting of garlic plant. L m denotes the moment from external force F to deflection center, m. It is assumed that EI varied with L and may be represented in the form of a Taylor-McLaurin polynomial [25] as follows:…”

Section: Figure 7 Model Diagram Of Garlic Plantmentioning

confidence: 99%

Design and test of the plant-correcting reel for harvesting lodging garlic plants

Li,

Liu

et al. 2024

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

Crops are prone to lodging with the decline of stem moisture and the intervention of other factors in the mature harvest period, such as garlic, which is difficult to harvest mechanically. To solve this problem, the plant-correcting reel for harvesting lodging garlic plants, bumped and deformed with plants many times to pull and lift them into a conveyor, was proposed in this study. This study analyzed the motion trajectory equation and key influencing factors of the reel and defined the lifting and plant-correcting stages as three processes of contact, stirring and release. For example, the contact deformation model and system energy equations were established in the contact process. Besides, in the stirring process, the garlic plantcorrecting conditions were established through the dynamic simulation test analysis of garlic seedling trajectories and the deflection model of garlic stem was constructed. Furthermore, in the release process, the expressions of rubber bars rotation and garlic plant offset bending curvature were constructed and the optimal number and distribution form of bars were determined. Meanwhile, the mechanism and key operating parameters of the auxiliary lifting mechanism of the divider were established. Through the single-factor test, the influence of reel speed, forward speed and reel height on the success feeding rate was analyzed under different bars distribution forms; Through multi-factor experiments, the interaction contour map of various factors was constructed. When reel speed, forward speed and reel height were 3 rad/s, 3.5 m/s, and 540 mm, the feeding success rate was 98.73%. The optimization factors were tested and verified, which met the operational requirements of a high feeding success rate and low loss rate of garlic harvest. This study combines laboratory virtual as well as field experiments and analyzes of trajectory of bars, contact deformation and deflection model of garlic plant, and reel rotation and garlic plant offset bending curvature to solve the problem of garlic lodging mechanized harvest and yield reduction.

show abstract

“…The mathematical models can analyze the interactions between different factors that influence food and agricultural systems (Golpira et al., 2021). For example, a Markov process‐based model was employed to simulate grain harvesting efficiency (Golpira & Sola‐Guirado, 2022).…”

Section: Introductionmentioning

confidence: 99%

Markov model planning on the adoption of an enhanced wheat cultivar

Golpira,

Rashidian,

Demmel

2023

Agronomy Journal

View full text Add to dashboard Cite

Predictions on the time needed to adopt new technologies can be used for planning purposes. These predictions should be calculated using a Markov planning model, which is a probabilistic approach to estimating the speed of adoption. In a research project, a data‐driven model was developed to simulate the adoption rate of an enhanced, drought‐resistant rain‐fed hard wheat (Triticum aestivum L.) cultivar in Kurdistan, Iran. The growers’ decision‐making on cultivar replacement was mapped onto the transition matrix, state probability, transition diagram, and tree of the states of the Markov process to compute the limiting probability of the stochastic model, thereby simulating the adoption rate of the wheat cultivar. The mathematical model, which achieves a convergence of 82% adoption for the enhanced wheat cultivar within 5 years, simulates farmers’ behavior and knowledge to enhance food security. Simulating the future of new technology in food and agricultural systems using the proposed methodology can assist policymakers in making informed decisions.

show abstract

Mathematical model-based redesign of chickpea harvester reel

Cited by 3 publications

References 18 publications

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Design and test of the plant-correcting reel for harvesting lodging garlic plants

Markov model planning on the adoption of an enhanced wheat cultivar

Contact Info

Product

Resources

About