LAUSR

Determination of ripeness represented by firmness measured during storage is important to guide the supply chain management of avocados. A machine vision system devised with a smartphone camera was used to capture images. Color values in L*a*b* were extracted from the images. Support vector regression (SVR), K-nearest neighbors regression (KNN), ridge regression, and lasso regression were compared for firmness prediction using the L*a*b* color space values. The results indicated the SVR performed the best among the four machine learning algorithms used. The SVR model predicted firmness of “Hass” avocado with R2, RMSE, and RPD of 0.92, 7.54, and 3.8 respectively for the model validation data set. It was concluded that the machine vision system devised with a smartphone camera and a SVR model could be a low-cost tool for the determination of ripeness of “Hass” avocado during harvest, storage, and distribution.