Predicting physicochemical properties of papayas ( <i>Carica papaya</i> L.) using a convolutional neural networks model approach

The current state of quality assessment methods for agricultural produce, particularly fruits, heavily relies on manual inspection techniques, which could be subjective, time-consuming, and prone to human errors. Consequently, there have been emerging trends and needs for non-destructive methods to evaluate fruit quality accurately and practically. This research aimed to develop a novel approach for predicting the physicochemical properties of papayas using a convolutional neural network (CNN) model that combines image analysis and weight assessment. This study involved capturing images of papayas at different ripening stages, measuring papaya weights, and determining various physicochemical properties such as texture, pH, total soluble solids, and seed weight. A total of 532 images were obtained from 132 papayas, and an additional 1064 images were generated through image augmentation. The dataset was divided into three sets with an 8:1:1 ratio for training, validation, and testing. The CNN model was trained using papaya images and weights as input values to predict and estimate the physicochemical property values. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R²) as metrics. The CNN model, integrated with image processing, could predict the diverse physicochemical properties of papayas with high accuracy. The MSE values estimated for the training and validation sets were 0.0284 and 0.1729, respectively. The R² values for the test dataset ranged from 0.71 to 0.94. These findings demonstrate that CNN-based models could provide detailed and quantitative insights, facilitating improved understanding and management of papaya quality while enhancing predictive modeling accuracy in agriculture. PRACTICAL APPLICATION: This research introduces a new method for accurately predicting the quality of papayas using a computer model. Instead of relying on manual inspection, which can be slow and prone to errors, this model uses images of papayas and their weights to predict properties, including texture, pH, total soluble solids, and seed weight. This can help manage papaya quality better while also improving agricultural production and transportation processes.