scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Plants Stress Response Detection by Selecting Minimal Bands of Hyperspectral Images

TL;DR: In this paper, a conditional covariance operator (CCM) was used to select the most significant spectral bands from the collected Hyperspectral data itself for plant stress analysis in rapid manner.
Abstract: It is an important task in the agricultural domain to determine the stress levels in plants. Drought conditions can have an adverse effect on crop yield. Hyperspectral Imaging (HSI) combined with classical Machine Learning algorithms are in current use to determine the stress levels. Every spectral band in an HSI does not contain useful information regarding the stress levels. For this reason, some vegetation indices are selected by agricultural researchers, based on reflectance ratios where a significant change in reflectance was observed because of stress. These indices do not always contain significant information because of changes in temperature, humidity or other atmospheric variations in different trials. There is no fixed set of vegetation indices which can be used to estimate stress levels accurately. In this paper, we demonstrated the working of Conditional Covariance Operator (CCM) which is used to select the most significant spectral bands from the collected Hyperspectral data itself. CCM is the most recent of the feature selection methods. This efficient feature selection method is used for the first time in this paper for plant stress analysis in rapid manner. It selects consistent discriminative spectral bands even when training examples per class are less than what other feature selection methods need. It can be seen that the Random Forest classifier model can classify the stress level into three categories (i) normal (ii) mild and (iii) severe stress with an accuracy of 99.7% when only 10 spectral bands are selected.
Citations
More filters
Journal ArticleDOI
01 Apr 2022-Plants
TL;DR: In this paper , the authors discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols, and discuss how artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis.
Abstract: Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

12 citations

References
More filters
Journal ArticleDOI
TL;DR: This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods and the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease.
Abstract: This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method.

324 citations

Journal ArticleDOI
TL;DR: In this paper emphasis is on estimation of canopy chlorophyll content and N content using remote sensing techniques and the CI2 was found to be a good and linear estimator of canopy N content.
Abstract: Plant stress is often expressed as a reduction in amount of biomass or leaf area index (LAI). In addition, stress may affect the plant pigment system, influencing the photosynthetic capacity of plants. Chlorophyll content is the main driver for this primary production. The chlorophyll content is indirectly related to the nitrogen (N) content. In this paper emphasis is on estimation of canopy chlorophyll content and N content using remote sensing techniques. Hyperspectral reflectance data representing a range of canopies were simulated using the PROSAIL radiative transfer model at a 1 nm sampling interval. Various indices were tested for estimating canopy chlorophyll content. Subsequently, tests with field data were performed for sampling locations within an extensively grazed fen meadow using ASD FieldSpec measurements and within a potato field with a Cropscan radiometer for estimating canopy N content. PROSAIL simulations showed that the red-edge chlorophyll index (CIred edge) was linearly related to the canopy chlorophyll content over the full range of potential values (R2=0.94) . In contrast, highly non-linear relationships of chlorophyll content with most traditional red-edge indices were found. At the study sites the CI2 was found to be a good and linear estimator of canopy N content (no chlorophyll was measured) for both the grassland site (R2=0.77) and for the potato field (R2=0.88) . The latter number refers to plots showing no “luxury” N consumption. However, for the full potato data set, including highly fertilized plants, an exponential relationship yielded a better fit (R2=0.85) as compared to a linear fit (R2=0.65) . Currently, this approach can, e.g., be applied with MERIS and Hyperion data and with the upcoming Sentinel-2 and -3 systems.

263 citations

Journal ArticleDOI
TL;DR: In this article, the authors used synthetic reflectance spectra generated by a radiative transfer model to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment.

259 citations


"Plants Stress Response Detection by..." refers background in this paper

  • ...Normalized difference vegetation index (NDVI) is the most used metric to estimate health status of a crop, stress on wheat vegetation can be detected by NDVI [5] [6] [7]....

    [...]

  • ...Hyperspectral imaging of a single plant occupies more than a gigabyte of space, the computational need of the analysis of the whole spectrum range will be massive hence we select some significant wavelengths and analyze the stress levels on those particular bands because the determination of the change in reflectance in biophysical parameters like water, nitrogen is significant [6]- [7], [1]- [4]....

    [...]

Journal ArticleDOI
TL;DR: An approach which combines unsupervised and supervised methods in order to identify several stages of progressive stress development from series of hyperspectral images, and it is shown that some VIs have overall relevance, while others are specific to particular senescence stages.
Abstract: Early stress detection in crop plants is highly relevant, but hard to achieve. We hypothesize that close range hyperspectral imaging is able to uncover stress related processes non-destructively in the early stages which are invisible to the human eye. We propose an approach which combines unsupervised and supervised methods in order to identify several stages of progressive stress development from series of hyperspectral images. Stress of an entire plant is detected by stress response levels at pixel scale. The focus is on drought stress in barley ( Hordeum vulgare ). Unsupervised learning is used to separate hyperspectral signatures into clusters related to different stages of stress response and progressive senescence. Whereas all such signatures may be found in both, well watered and drought stressed plants, their respective distributions differ. Ordinal classification with Support Vector Machines (SVM) is used to quantify and visualize the distribution of progressive stages of senescence and to separate well watered from drought stressed plants. For each senescence stage a distinctive set of most relevant Vegetation Indices (VIs) is identified. The method has been applied on two experiments involving potted barley plants under well watered and drought stress conditions in a greenhouse. Drought stress is detected up to ten days earlier than using NDVI. Furthermore, it is shown that some VIs have overall relevance, while others are specific to particular senescence stages. The transferability of the method to the field is illustrated by an experiment on maize ( Zea mays ).

189 citations


"Plants Stress Response Detection by..." refers background in this paper

  • ...of only the useful bands can help in determining the stress tolerance of different genotypes more rapidly [10] [11]....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the role of exogenous glutathione (GSH) in conferring high temperature stress (HT, 42°C) tolerance in mung bean (Vigna radiata L. cv. Binamoog-1) seedlings was investigated.

188 citations