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首页> 外文期刊>Journal of Applied Life Sciences International >Application of Artificial Neural Networks to Estimate Color Surface Features of Three Maturity Stages of Tomato Based on Dimensions and Weight
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Application of Artificial Neural Networks to Estimate Color Surface Features of Three Maturity Stages of Tomato Based on Dimensions and Weight

机译：基于尺寸和权重的人工神经网络在番茄三个成熟阶段的颜色表面特征估计中的应用

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Aims: The aim of this research was to investigate the effect on application of artificial neutral network (ANN) to estimate the color surface of fruit of three maturity stages of tomatoes based on fruit dimensions (length and width) and weight. Study Design: Simple machine vision system was built to extract color surface features of tomato samples. Place of Study: Agricultural and Bio-systems Engineering Department, Faculty of Agriculture, Alexandria University, Egypt. Methodology: Samples of variety of tomatoes (Baladi variety) were manually harvested from the field at Educo, El- Beheira Governorate, Egypt. Three maturity stages of the variety were harvested in different days by eye inspection based on their color. The maturity stages were green, pink and red. The weight and dimensions of each maturity samples were measured. Samples images were taken on a white background and manual mode, no zoom, no flash were used by the camera. Surface color of the tomato samples was analyzed quantitatively. ANN model to estimate the surface color was applied. Results: The evaluation results of testing data set showed that ANN could be able to estimate color surface features of tomatoes at different accuracy as evaluated by coefficient of determination (R2) of 0.7161, 0.8273, 0.8605, 0.5448, 0.8056, 0.7954 and 0.854, respectively for L*, a* b* Hue, Chroma, color index and color difference with true red. The obtained weights from the ANN training process were formulated in Excel spreadsheet. Conclusion: The studied color surface features of tomato for three maturity stages and input variables well correlated. The tomato weight contributed significantly in estimating all surface color features of tomato compared to the length and width. The developed Excel spreadsheet could be used as a quick tool to estimate color surface features of tomato.
机译：目的：本研究的目的是研究基于水果尺寸（长和宽）和重量，应用人工中性网络（ANN）评估番茄三个成熟阶段的水果色泽的影响。研究设计：建立了简单的机器视觉系统来提取番茄样品的有色表面特征。研究地点：埃及亚历山大大学农业系农业与生物系统工程系。方法：从埃及El-Beheira省Educo的田间手动收获各种西红柿（Baladi品种）的样品。根据颜色，通过肉眼检查在不同的日期收获了该品种的三个成熟阶段。成熟阶段是绿色，粉红色和红色。测量每个成熟样品的重量和尺寸。样本图像是在白色背景和手动模式下拍摄的，相机没有变焦，没有闪光灯。定量分析番茄样品的表面颜色。应用了用于估计表面颜色的ANN模型。结果：测试数据集的评估结果表明，人工神经网络能够以0.7161、0.8273、0.8605、0.5448的测定系数（R 2 ）评估不同精度的番茄色泽特征。分别为L *，a * b *色相，色度，色指数和真红色的色差分别为0.8056、0.7954和0.854。从ANN训练过程中获得的权重在Excel电子表格中制​​定。结论：研究的番茄三个成熟阶段的颜色表面特征与输入变量具有良好的相关性。与长度和宽度相比，番茄的重量在估计番茄的所有表面颜色特征方面做出了重要贡献。开发的Excel电子表格可以用作评估番茄颜色表面特征的快速工具。

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