Nouri Seyedeh Fatemeh, Mehdizadeh Saman Abdanan, Ampatzidis Yiannis
Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz 63417-73637, Iran.
Agricultural and Biological Engineering Department, Southwest Florida Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, 2685 SR 29 North, Immokalee, FL 34142, USA.
Sensors (Basel). 2025 Aug 25;25(17):5279. doi: 10.3390/s25175279.
Accurate and non-destructive assessment of fruit firmness is critical for evaluating quality and ripeness, particularly in postharvest handling and supply chain management. This study presents the development of an image-based vibration analysis system for evaluating the firmness of kiwifruit using computer vision and machine learning. In the proposed setup, 120 kiwifruits were subjected to controlled excitation in the frequency range of 200-300 Hz using a vibration motor. A digital camera captured surface displacement over time (for 20 s), enabling the extraction of key dynamic features, namely, the damping coefficient (damping is a measure of a material's ability to dissipate energy) and natural frequency (the first peak in the frequency spectrum), through image processing techniques. Results showed that firmer fruits exhibited higher natural frequencies and lower damping, while softer, more ripened fruits showed the opposite trend. These vibration-based features were then used as inputs to a feed-forward backpropagation neural network to predict fruit firmness. The neural network consisted of an input layer with two neurons (damping coefficient and natural frequency), a hidden layer with ten neurons, and an output layer representing firmness. The model demonstrated strong predictive performance, with a correlation coefficient (R) of 0.9951 and a root mean square error (RMSE) of 0.0185, confirming its high accuracy. This study confirms the feasibility of using vibration-induced image data combined with machine learning for non-destructive firmness evaluation. The proposed method provides a reliable and efficient alternative to traditional firmness testing techniques and offers potential for real-time implementation in automated grading and quality control systems for kiwi and other fruit types.
准确且无损地评估果实硬度对于评价果实品质和成熟度至关重要，特别是在采后处理和供应链管理中。本研究介绍了一种基于图像的振动分析系统的开发，该系统利用计算机视觉和机器学习来评估猕猴桃的硬度。在所提出的设置中，使用振动电机对120个猕猴桃在200 - 300Hz的频率范围内进行受控激励。一台数码相机记录随时间的表面位移（持续20秒），通过图像处理技术能够提取关键动态特征，即阻尼系数（阻尼是材料耗散能量能力的一种度量）和固有频率（频谱中的第一个峰值）。结果表明，较硬的果实表现出较高的固有频率和较低的阻尼，而较软、成熟度更高的果实则呈现相反的趋势。然后将这些基于振动的特征用作前馈反向传播神经网络的输入，以预测果实硬度。该神经网络由一个具有两个神经元（阻尼系数和固有频率）的输入层、一个具有十个神经元的隐藏层以及一个表示硬度的输出层组成。该模型表现出很强的预测性能，相关系数（R）为0.9951，均方根误差（RMSE）为0.0185，证实了其高精度。本研究证实了使用振动诱导图像数据结合机器学习进行无损硬度评估的可行性。所提出的方法为传统硬度测试技术提供了一种可靠且高效的替代方案，并为猕猴桃及其他水果类型在自动分级和质量控制系统中的实时应用提供了潜力。
