Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

Abstract

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.

Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge

“…The adoption of non-destructive data acquisition techniques offers advantages such as reduced experimental costs and shortened research cycles [6][7][8] . However, traditional nondestructive approaches for tomato ripeness detection often rely on labor-intensive manual selection and judgment [9][10][11] . These methods are susceptible to subjective factors and reduced efficiency, resulting in lower detection speed and accuracy.…”