Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements

© 2014 by the authors. Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of the prevalent diseases and has different symptoms including yellow, orange, dark brown, and dry areas. The reflectance spectrum data for healthy and infected leaves were collected using a spectroradiometer in the 450 to 1000 nm range. The ratio of the disease-affected area to the total leaf area and the proportion of each disease symptoms were obtained using RGB digital images. As the disease severity increases, so does the scattering of all SVI values. The indices were categorized into three groups based on their accuracies in disease detection. A few SVIs showed an accuracy of more than 60% in classification. In the first group, NBNDVI, NDVI, PRI, GI, and RVSI showed the highest amount of classification accuracy. The second and third groups showed classification accuracies of about 20% and 40% respectively. Results show that few indices have the ability to indirectly detect plant disease

Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina)

Spectral vegetation indices (SVIs) have been widely used to detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the final symptom is when the leaf becomes dry. The goal of this work is to develop spectral disease indices for the detection of leaf rust. The reflectance spectra of the wheat's infected and non-infected leaves at different disease stages were collected using a spectroradiometer. As ground truth, the ratio of the disease-affected area to the total leaf area and the fractions of the different symptoms were extracted using an RGB digital camera. Fractions of the various disease symptoms extracted by the digital camera and the measured reflectance spectra of the infected leaves were used as input to the spectral mixture analysis (SMA). Then, the spectral reflectance of the different disease symptoms were estimated using SMA and the least squares method. The reflectance of different disease symptoms in the 450~1000 nm were studied carefully using the Fisher function. Two spectral disease indices were developed based on the reflectance at the 605, 695 and 455 nm wavelengths. In both indices, the R2 between the estimated and the observed was as highas 0.94. © 2014 by the authors; licensee MDPI, Basel, Switzerland

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

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

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Developing an Index for Detection and Identification of Disease Stages

Spectral data have been widely used to estimate the disease severity (DS) levels of different plants. However, such data have not been evaluated to estimate the disease stages of the plant. This study aimed at developing a spectral disease index (SDI) that is able to identify the stages of wheat leaf rust disease at various DS levels. To meet the aim of the study, the reflectance spectra (350-2500 nm) of infected leaves with different symptom fractions and DS levels were measured with a spectroradiometer. Then, pure spectra of the different disease symptoms at the leaf scale were analyzed, and a new function was developed to find the wavelengths most sensitive to disease symptom fraction. The reflectance spectra with highest sensitivity were found at 675 and 775 nm. Finally, the normalized difference of DS and the ratio ρ675/ρ775 was used as a new SDI to discriminate three different levels of the disease stage at the canopy level. The suggested SDI showed a promising performance to improve the detection disease stages in precision plant protection

Integrated switched-capacitor-based cold-start circuit for DC-DC energy harvesters with wide input/output voltage range and low inductance in 40-nm CMOS

\u3cp\u3eThis paper outlines an integrated switched-capacitor (SC)-based cold-start circuit for dc-dc energy harvesters that uniquely combines a low cold-start voltage, wide input/output voltage and low inductance value in the boost stage. The proposed design specifically targets size-constrained, self-powered Internet-of-Things applications. The proposed design is an SC circuit built from low-threshold-voltage devices operating in the sub-threshold region and provides the drive voltage for high-threshold-voltage devices of the boost dc-dc converter. The SC circuit is an NMOS-based Dickson charge pump driven synchronously with a series of cross-coupled voltage doublers and voltage multiplying gate drivers. The SC circuit, which is integrated together with a boost converter as the dc-dc energy harvester, has been implemented in a 40-nm CMOS process for future system-on-chip integration. The measured results show that with a 4.7 μH boost converter inductance, the design can start up from typical input voltages as low as 190 mV while offering up to 2.4 V input and 5 V output voltage compatibility.\u3c/p\u3

A new phenology-based method for mapping wheat and barley using time-series of Sentinel-2 images

In recent years, various techniques have been developed to generate crop-type maps based on remote sensing data. Wheat and barley are two major cereal crops cultivated as the first and fourth largest grain crops across the globe. The variations in spectral temporal profile of both crops are generally insignificant at small scales and therefore the two crops are phenologically fairly clearly separated; however, at large scale areas the variance of phenological parameters increases for both crops due to the effects of various climatic and orographic factors which adversely influences discrimination of wheat and barley. Additionally, wheat and barley are usually cultivated as both spring and winter or early and late season crops in some areas, making it more difficult to distinguish them. Therefore, developing a new method based on remote sensing data for effective discrimination of wheat and barley is an important necessity in the field of precision agriculture. To this end, this research presents a new phenology-based method to discriminate barley from wheat. In this study, Sentinel-2 (S2) time-series data of a study site in Iran (Markazi) and two sites in the USA (Idaho and North California), are employed. Spectral reflectance values of wheat and barley are examined during the growing season and a new spectral-temporal feature is successfully developed for automatic identification of the barley heading date. The Relief-f algorithm is then employed to select appropriate spectral features of S2 to distinguish wheat from barley at the heading date. Finally, generated spectral features at the heading date are used as input to Support Vector Machine (SVM) and Random Forest (RF) to produce barley and wheat maps. The Kappa coefficient and overall accuracy (OA) obtained for the three study sites are more than 0.67 and 76%, respectively. The findings of this study demonstrate the potential of remote sensing data to identify the phenological growth stages of barley and distinguish it successfully from wheat