Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, USA.
Cobslab, Goyang-si, South Korea.
J Food Sci. 2024 Nov;89(11):7861-7871. doi: 10.1111/1750-3841.17462. Epub 2024 Oct 16.
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 (R) 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 R 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.
农产品（尤其是水果）质量评估方法目前主要依赖于人工检测技术，但这种方法可能存在主观性、耗时和容易出错等问题。因此，人们越来越需要开发非破坏性的方法来准确、实际地评估水果的质量。本研究旨在开发一种新方法，通过卷积神经网络（CNN）模型结合图像分析和权重评估来预测木瓜的理化性质。该研究通过采集不同成熟阶段木瓜的图像、测量木瓜的重量以及测定各种理化性质（如质地、pH 值、总可溶性固形物和种子重量）来进行。共从 132 个木瓜中获取了 532 张图像，并通过图像扩充生成了 1064 张额外的图像。数据集分为三部分，比例为 8:1:1，分别用于训练、验证和测试。将木瓜的图像和重量作为输入值来训练 CNN 模型，以预测和估计理化性质值。使用均方误差（MSE）和决定系数（R）作为指标来评估模型性能。将图像处理与 CNN 模型相结合，可以准确地预测木瓜的多种理化性质。训练集和验证集的 MSE 值分别为 0.0284 和 0.1729，测试集的 R 值范围为 0.71 到 0.94。这些发现表明，基于 CNN 的模型可以提供详细和定量的见解，有助于更好地理解和管理木瓜的质量，同时提高农业预测建模的准确性。实际应用：本研究介绍了一种使用计算机模型准确预测木瓜质量的新方法。该模型不依赖于可能缓慢且容易出错的人工检查，而是使用木瓜的图像和重量来预测包括质地、pH 值、总可溶性固形物和种子重量在内的特性。这有助于更好地管理木瓜的质量，同时改进农业生产和运输过程。
