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图像处理和对终端热应激对豆产量的影响分析

Hena Gain¹, Ruturaj Nivas Patil¹, Konduri Malik¹

¹Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, 721301 India.

|August 2, 2024

概括

机器学习模型使用植物图像和植物指数预测的产量,识别关键特征,如每种植物的产量和每种植物的,以便在热应激条件下进行早期预测.

科学领域:

农业科学农业科学
植物科学植物科学
机器学习应用机器学习应用

背景情况:

非生物因素,特别是热应激,在全球范围内显著影响着的生长,发育和产量.
由于复杂的相互作用,在压力条件下预测的特征仍然是一个挑战.
了解热应激影响对于改善作物和粮食安全至关重要.

研究的目的:

开发一种基于机器学习的模型,用于预测豆产量.
评估高温压力对生长和产量属性的影响.
为了确定用于早期生产预测的关键产量属性.

主要方法:

利用图像分析技术 (过量绿色,修改过量绿色 (ME×G),植物提取的颜色指数 (CIVE)) 来提取35种植物指数.
开发了随机森林 (RF) 和人工神经网络 (ANN) 模型用于产量预测.
在正常播种和晚播种 (热应力) 条件下比较模型性能.

主要成果:

使用Otsu的值的ME×G-CIVE方法显示出优越的图像细分性能.
随机森林 (RF) 模型展示了对产量预测的最高概括能力.
每种植物的产量和每种植物的豆数量被确定为早期豆产量预测的最重要的属性.
与正常播种的植物相比,晚播种 (热应激) 的植物在大多数产量参数中表现出显著的减少.
耐热基因型 (RLG-05,Kota Masoor-1,Kota Masoor-2) 的产量和收获指数的降低比热敏基因型 (HUL-57) 的降低要小.

结论:

机器学习,特别是射频模型,可以有效地使用图像衍生植物指数预测豆产量.
通过专注于每棵植物的产量和每棵植物的豆数量,可以预测豆产量的早期预测.
热应激显著降低了豆产量,强调了识别和培育耐热品种的重要性.
需要进一步的研究,以整合更多的压力调节属性,以提高预测准确度.

关键词:

收获指数收获指数热应激是一种热应激.豆豆豆是一个机器学习是机器学习.植物图像分析分析收益率收益率是指收益率.

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