Prediction of size and mass of pistachio kernels using random Forest machine learning
2020
Vidyarthi, Sriram K. | Tiwari, Rakhee | Singh, Samrendra K. | Xiao, Hong‐Wei
The size and mass of the pistachio kernels are important parameters in pistachio industry worldwide, which are generally associated with pistachio classification and consumer's approval. In this study, we proposed a technique that accurately measures the size and mass of raw pistachio kernels simultaneously using image processing and machine‐learning ensemble. An image‐processing algorithm applying recursive method was used to detect a pistachio kernel from an image and estimate its size based on the pixels occupied. The number of pixels representing a pistachio kernel was used as its digital impression to estimate its size and mass. We employed Random Forest (RF) model to predict the mass of the individual pistachio kernels based on the patterns derived from the pistachio pixels in the image. The mean measured length (18.002 mm) of 100 pistachio kernels were close to the RF predicted length (18.608 mm). Similar close correlation for individual measured pistachio kernel mass and predicted mass was noticed having a bootstrapped residual of 0.036 ± 0.004 g with a class interval of 0.0358–0.0362 at 95% confidence level. Compared to the regression model, bootstrapping and uncertainty quantification showed a good prediction accuracy and robustness of the RF model. PRACTICAL APPLICATIONS: The size of pistachio kernels is a crucial factor in defining pistachio grades commercially. Current pistachio grading in industry is still performed manually, which is tedious, time consuming, labor intensive and subject to human error and inconsistency. The developed machine learning algorithm based on Random Forest combined with image processing in this study can be a very useful tool in pistachio industry for rapid classification of pistachio worldwide.
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