Md Nasim Reza, M. Chowdhury, Sumaiya Islam, Md Shaha Nur Kabir, Sang Un Park, Geung-Joo Lee, Jongki Cho, Sun-Ok Chung
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A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.","PeriodicalId":13034,"journal":{"name":"Horticulturae","volume":"1 3","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network\",\"authors\":\"Md Nasim Reza, M. Chowdhury, Sumaiya Islam, Md Shaha Nur Kabir, Sang Un Park, Geung-Joo Lee, Jongki Cho, Sun-Ok Chung\",\"doi\":\"10.3390/horticulturae9121346\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. 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The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. 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引用次数: 0
摘要
叶片是植物的主要部分,检查叶片面积对于了解植物的生长和生理学至关重要。准确估算叶面积是了解植物生长的关键。本研究提出了一种利用图像处理和人工神经网络(ANN)模型无损估算五色草植物叶面积的方法。图像处理方法包括灰度转换、直方图均衡化、二值掩蔽和区域填充等一系列步骤,准确率达到约 96.6%。使用 70% 的数据集训练的 ANN 模型在训练阶段和测试阶段分别表现出 97.1% 和 96.6% 的高相关性,叶片的长度和宽度对模型输出有显著影响。对比分析表明,与图像处理方法相比,ANN 模型的性能更优越,R2 值更高(大于 0.99),误差更小。此外,它还显示了不同 LED 光组合和营养水平(电导率,EC)对五角枫植物生长的影响,表明 R70:B30 LED 光比和营养水平 2(2.0 dS-m-1)对五角枫植物的生长最为有利。基于易于获得的长度和宽度测量值估算叶面积的 ANN 模型具有非破坏性、简便性和快速性,因此是在受控环境中评估植物生长情况的便捷而准确的工具。这种方法为未来的研究提供了机会,可以在不伤害植物的情况下跟踪不同生长条件下叶面积的变化,从而提高研究的精确度。
Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network
The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.