Precision harvesting: comparative analysis of machine learning and generative AI-based classifiers for guava fruit maturity assessment using thermal imaging

Abstract

Guava is a highly nutritious fruit with abundance of health benefits and is medically recommended for daily consumption. Therefore, quality assessment of guava is of significance. Manual quality grading of fruits is being performed for a long time which suffers from subjective biases. Consequently, development of a computer vision-based automated system for quality grading of fruits is essential. In this paper, maturity assessment of guava is performed using various machine learning classifiers, including neural network, fuzzy logic system, and generative AI models. Thermal images of guava are used for maturity assessment. Performance of these models is evaluated by determining the confusion matrix and classification report, considering both class-wise and overall classification. After careful observation, FLS-based ANFIS is highly recommended for grading the guava on the basis of its maturity level. Furthermore, thermal imaging is also very significant for the development of a holistic computer vision-based fruit quality assessment model.