Assessing kiwifruit quality in storage through machine learning

Abstract Today, diets rich in fruits and vegetables are highly recommended since they contribute to health. The high deterioration rate of fruits distinguishes them from other crops. This study aimed to evaluate the effects of various storage conditions on some quality attributes of kiwifruit through machine learning (ML) using support vector machine (SVM) models. Kiwifruits were subjected to quasi‐static loading and coated with different coatings, such as grape, date, and mulberry syrups. Then, the coated kiwifruits were stored at humidity levels of 90% and 95% in completely dark and bright environments with a Compact fluorescent lamp (CFL) bulb for 5, 10, and 15 days. Once the storage period had been completed, quality attributes of the kiwifruit, including antioxidant content, phenolic content, total soluble solids (TSS), pH, and firmness were measured. Each test was performed three times. The numerical results were analyzed through an ML approach using an SVM model on MATLAB. To predict the physical properties of kiwifruit using storage conditions and vice versa, it was found that the most accurate SVM model with a linear kernel predicted the weight loss of kiwifruit based on storage conditions, with the coefficient of determination ( R 2 ) being 0.54. To predict the biochemical properties using the storage conditions and vice versa, it was found that kiwifruit firmness was most accurately predicted by the SVM model with the Gaussian kernel, with an R 2 of 0.70. Moreover, humidity and storage duration were modeled by SVMs with linear kernels, calculating the coefficients of determination to be 0.39 and 0.90, respectively. To predict biochemical properties using physical properties and vice versa, it was observed that the weight loss was more accurately predicted by an SVM with a linear kernel, with an R 2 ‐value of 0.76. Reliable results were not obtained for further research for the other modeled parameters using an SVM approach. Practical Applications It was found that light was most accurately predicted by the linear SVM. Storage conditions revealed SVM (linear kernel) accurately predicting kiwifruit weight loss. Linear and Gaussian SVMs accurately modeled phenolic and antioxidant content, R 2 ‐values: 0.73 and 0.34, respectively.