Smart and accurate: A new tool to identify stressed soybean seeds based on multispectral images and machine learning models

IF 6.3 Q1 AGRICULTURAL ENGINEERING
Ana Carolina Picinini Petronilio , Clíssia Barboza Mastrangelo , Thiago Barbosa Batista , Gustavo Roberto Fonseca de Oliveira , Isabela Lopes dos Santos , Edvaldo Aparecido Amaral da Silva
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Abstract

Extreme environmental conditions have been recurrent during the last few years and have impacted crop seed quality worldwide, mainly but not limited to, soybeans (Glycine max (L) Merrill). To overcome this, seed companies often demand innovative tools to address seed quality factors. Machine learning models based on multispectral imaging are a novel seed quality analysis approach. Thus, we hypothesize that segmenting stressed (those produced under conditions that are not favorable to the mother-plant) and non-stressed (produced under conditions favorable to the mother-plant) soybean seeds would be possible with this technology, opening a new opportunity for seed quality management and elucidating quality factors. Soybean seeds (cultivar BR/MG 46-Conquista) were produced under water deficit and heat during maturation (from R5.5 onwards). Multispectral images were acquired from stressed and non-stressed seeds, and the reflectance, autofluorescence, physical properties, and chlorophyll parameters were extracted from the images. In parallel, we determined seed vigor. We designed machine learning models using multispectral imaging data based on three algorithms: neural network, support vector machine, and random forest. Our results demonstrated that the stressed seeds have spectral markers that enable their recognition. Concomitantly, these markers had a direct relationship with seed vigor. The machine learning models developed based on neural network algorithm showed the highest performance in segmenting stressed seeds (≥90 % of accuracy, precision, recall, specificity and F1 score) in contrast to random forest- and support vector machine algorithm (≥88 % of accuracy, precision, recall, specificity and F1 score). Here, we report a new approach for multispectral imaging with the potential to identify soybean seeds of lower vigor as a result of unfavorable environmental conditions during seed maturation.

Abstract Image

智能和准确:一种基于多光谱图像和机器学习模型识别受压大豆种子的新工具
在过去的几年里,极端的环境条件反复出现,影响了世界范围内作物的种子质量,主要但不限于大豆(Glycine max (L) Merrill)。为了克服这个问题,种子公司通常需要创新的工具来解决种子质量因素。基于多光谱成像的机器学习模型是一种新的种子质量分析方法。因此,我们假设利用该技术可以将胁迫(在不利于母株的条件下生产的)和非胁迫(在有利于母株的条件下生产的)大豆种子进行分割,为种子质量管理和阐明质量因素开辟了新的机会。大豆种子(品种BR/MG 46-征服者)在成熟期(R5.5起)缺水和高温条件下生产。获取胁迫和非胁迫种子的多光谱图像,提取其反射率、自身荧光、物理性质和叶绿素参数。同时,我们测定了种子的活力。我们基于神经网络、支持向量机和随机森林三种算法设计了基于多光谱成像数据的机器学习模型。我们的结果表明,胁迫种子具有光谱标记,使其能够识别。同时,这些标记与种子活力有直接关系。与随机森林和支持向量机算法(准确率、精密度、召回率、特异性和F1分数≥88%)相比,基于神经网络算法开发的机器学习模型在逆境种子分割方面表现出最高的性能(准确率、精密度、召回率、特异性和F1分数≥90%)。在这里,我们报告了一种新的多光谱成像方法,该方法有可能识别由于种子成熟过程中不利的环境条件而导致活力较低的大豆种子。
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