Noninvasive Detection of Salt Stress in Cotton Seedlings by Combining Multicolor Fluorescence-Multispectral Reflectance Imaging with EfficientNet-OB2.

IF 7.6 1区 农林科学 Q1 AGRONOMY
Plant Phenomics Pub Date : 2023-12-08 eCollection Date: 2023-01-01 DOI:10.34133/plantphenomics.0125
Jiayi Li, Haiyan Zeng, Chenxin Huang, Libin Wu, Jie Ma, Beibei Zhou, Dapeng Ye, Haiyong Weng
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Abstract

Salt stress is considered one of the primary threats to cotton production. Although cotton is found to have reasonable salt tolerance, it is sensitive to salt stress during the seedling stage. This research aimed to propose an effective method for rapidly detecting salt stress of cotton seedlings using multicolor fluorescence-multispectral reflectance imaging coupled with deep learning. A prototyping platform that can obtain multicolor fluorescence and multispectral reflectance images synchronously was developed to get different characteristics of each cotton seedling. The experiments revealed that salt stress harmed cotton seedlings with an increase in malondialdehyde and a decrease in chlorophyll content, superoxide dismutase, and catalase after 17 days of salt stress. The Relief algorithm and principal component analysis were introduced to reduce data dimension with the first 9 principal component images (PC1 to PC9) accounting for 95.2% of the original variations. An optimized EfficientNet-B2 (EfficientNet-OB2), purposely used for a fixed resource budget, was established to detect salt stress by optimizing a proportional number of convolution kernels assigned to the first convolution according to the corresponding contributions of PC1 to PC9 images. EfficientNet-OB2 achieved an accuracy of 84.80%, 91.18%, and 95.10% for 5, 10, and 17 days of salt stress, respectively, which outperformed EfficientNet-B2 and EfficientNet-OB4 with higher training speed and fewer parameters. The results demonstrate the potential of combining multicolor fluorescence-multispectral reflectance imaging with the deep learning model EfficientNet-OB2 for salt stress detection of cotton at the seedling stage, which can be further deployed in mobile platforms for high-throughput screening in the field.

将多色荧光-多光谱反射成像与 EfficientNet-OB2 结合使用,对棉花幼苗的盐胁迫进行无创检测。
盐胁迫被认为是棉花生产的主要威胁之一。尽管棉花具有合理的耐盐性,但它在幼苗期对盐胁迫非常敏感。本研究旨在提出一种利用多色荧光-多光谱反射成像结合深度学习快速检测棉花幼苗盐胁迫的有效方法。该研究开发了一个可同步获取多色荧光和多光谱反射图像的原型平台,以获得每株棉花幼苗的不同特征。实验发现,盐胁迫对棉花幼苗造成危害,盐胁迫17天后,棉花幼苗丙二醛含量增加,叶绿素含量、超氧化物歧化酶和过氧化氢酶含量下降。为了减少数据维度,研究人员采用了Relief算法和主成分分析法,前9个主成分图像(PC1至PC9)占原始变化的95.2%。根据 PC1 至 PC9 图像的相应贡献率,优化分配给第一次卷积的卷积核数量,从而建立了优化的 EfficientNet-B2(EfficientNet-OB2),专门用于固定的资源预算,以检测盐胁迫。对于 5 天、10 天和 17 天的盐胁迫,EfficientNet-OB2 的准确率分别达到了 84.80%、91.18% 和 95.10%,超过了训练速度更快、参数更少的 EfficientNet-B2 和 EfficientNet-OB4。这些结果证明了将多色荧光-多光谱反射成像与深度学习模型EfficientNet-OB2相结合用于棉花苗期盐胁迫检测的潜力,可进一步部署在移动平台上进行田间高通量筛选。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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