The loss of time during harvesting processes can affect the effective field capacity. This study aimed to observe and analyse the time that is lost during harvesting processes utilizing Thai combine harvesters. The random data was collected in November 2017 from 28 Thai combine harvesters harvesting Khao Dowk Mali 105 and in May 2017 from 27 Thai combine harvesters harvesting Phitsanulok-2. The theoretical field time, the headland turning time, the un-harvest moving and unloading time, the adjustment and maintenance time, and the bund crossing time were all recorded. The results indicated that the headland turning time and the un-harvest moving and unloading time had been the major influences that had been responsible for the increases in total time and had resulted in decreases in the effective field capacity. The times for adjustment and maintenance and for bund crossing had shown only minor losses.
This research proposes an artificial neural network (ANN)-based repair and maintenance (R&M) cost estimation model for agricultural machinery. The proposed ANN model can achieve high estimation accuracy with small data requirement. In the study, the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters. The model inputs are geographical regions, harvest area, and curve fitting coefficients related to historical cost data; and the ANN output is the estimated R&M cost. Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm. The R&M costs are estimated using the ANN-based model, and results are compared with those of conventional mathematical estimation model. The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%, indicating the proposed ANN model's high predictive accuracy. The proposed ANN-based model is useful for setting the service rates of agricultural machinery, given the significance of R&M cost in profitability. The novelty of this research lies in the use of curve-fitting coefficients in the ANNbased estimation model to improve estimation accuracy. Besides, the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility. Moreover, with minor modifications, the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.
This research explores two operating cost estimation models as a function of repair and maintenance (R&M) outlay for domestically-made rice combine harvesters: ASAE-based and power function-fitted estimation models. The goal is to establish a guideline for operating cost estimation suited to the agricultural context of Thailand. In the study, variable cost data belonged to the country’s northeastern region. Simulations were carried out using MATLAB/Simulink and results compared. The ASAE-based operating cost estimations were consistently larger than those of power function-fitted model, and the divergence became more pronounced with increase in harvest area. The finding could be attributed to country-specificity in which R&M practices varied from country to country. Essentially, the power function-fitted estimation model is more suited to the agricultural context of Thailand, in comparison with the ASAE-based model which tends to overestimate the cost figure.
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