Discrimination of herbicide-resistant kochia with hyperspectral imaging

Nugent, Paul W.; Shaw, Joseph A.; Jha, Prashant; Scherrer, Bryan; Donelick, Andrew; Kumar, Vipan

doi:10.1117/1.jrs.12.016037

Cited by 18 publications

(14 citation statements)

References 24 publications

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“…Since these overlapping or adjoining bands are nearly 99% correlated, there is a strong match between this finding and earlier findings by [54,79]. Earlier studies [29,55,[92][93][94] have demonstrated the value of these and similar OHNBs (Table 6) in a wide array of cropland studies. For example, the bands around 447 nm, 488 nm, 529 nm, and 569 nm are useful for estimating nitrogen and thus pigment content, as well as assessing light use efficiency and stress levels ( Table 6).…”

Section: Discussionsupporting

confidence: 86%

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Aneece

Thenkabail

2018

Remote Sensing

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As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

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Section: Discussionsupporting

confidence: 86%

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Aneece

Thenkabail

2018

Remote Sensing

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“…1, our previous work applied an SVM to this problem, and the results were promising. 10 Even so, those results suggested that there may be room for improvement, thus motivating the current work applying a feedforward neural network. As will be demonstrated later, the results from the neural network yielded substantial improvement over the SVM.…”

Section: Neural Networkmentioning

confidence: 95%

“…All kochia, marestail, and common lambsquarters plants used in the experiments were grown using the method detailed in our earlier paper. 10 During the 2017 and 2018 data collection in the field, we imaged potted kochia plants (at identical growth stages) among sugar beet at the full canopy growth stage, this time in direct sunlight. Hyperspectral images were collected individually for crops, and herbicide-resistant versus herbicide-susceptible kochia, as well as for a combination of various crops and different kochia biotypes.…”

Section: Physical Experiments and Data Collectionmentioning

confidence: 99%

“…Our previous work 10 classified between mature herbicide-susceptible and herbicide-resistant biotypes of the weed kochia, which has been demonstrated to be resistant to both glyphosate 11,12 (commonly marketed as Roundup ® ) and dicamba (the active ingredient in the herbicides Banvel ® and Clarity ® ). There we used ground-based hyperspectral imaging and a support vector machine (SVM) learning algorithm to correctly identify herbicide-susceptible kochia, glyphosate-resistant kochia, and dicamba-resistant kochia with 80%, 76%, and 67% classification accuracies, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral imaging and neural networks to classify herbicide-resistant weeds

Scherrer

Sheppard

Jha

et al. 2019

J. Appl. Rem. Sens.

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A segment of the field of precision agriculture is being developed to accurately and quickly map the location of herbicide-resistant and herbicide-susceptible weeds using advanced optics and computer algorithms. In our previous paper, we classified herbicide-susceptible and herbicide-resistant kochia [Bassia scoparia (L.) Schrad.] using ground-based hyperspectral imaging and a support vector machine learning algorithm, achieving classification accuracies of up to 80%. In our current work, we imaged kochia along with marestail (also called horseweed) [Conyza canadensis (L.) Cronquist] and common lambsquarters (Chenopodium album L.) and the crops barley, corn, dry pea, garbanzo, lentils, pinto bean, safflower, and sugar beet, all of which were grown at the Southern Agricultural Research Center in Huntley, Montana. These plants were imaged using both ground-based and drone-based hyperspectral imagers and were classified using a neural network machine learning algorithm. Depending on what plants were imaged, the age of the plants, and lighting conditions, the classification accuracies ranged from 77% to 99% for spectra acquired on our ground-based imaging platform and from 25% to 79% on our drone-based platform. These accuracies were generally highest when imaging younger plants.

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“…Some relevant applications include detecting fungi that cause citrus fruit to rot, 21 identifying mechanical damage to mangoes, 22 classifying produce type, 23 and evaluating tomato ripeness. 11,24 In this paper, we build on our recent work in agricultural remote sensing 25,26 to expand the analysis of fruits and vegetables using hyperspectral imaging and machine learning. In particular, we develop a framework for analyzing and predicting the ripeness of produce commonly available in the retail grocery store setting, focusing on bananas, potatoes, tomatoes, avocados, and bell peppers.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging and machine learning for monitoring produce ripeness

Logan¹,

Scherrer²,

Senecal³

et al. 2020

Sensing for Agriculture and Food Quality and Safety XII

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Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process that uses a visible near-infrared (VNIR) hyperspectral imager from Resonon, Inc., coupled with machine learning algorithms to assess the ripeness of various pieces of produce. The images were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using RGB images, full spectrum hyperspectral images, and the genetic algorithm feature selection method. Results showed that the genetic algorithm-based feature selection method outperforms RGB images for all tested produce, outperforms hyperspectral imagery for bananas, and matches hyperspectral imagery performance for green peppers. This feature selection method is being used to develop a low-cost multi-spectral imager for use in monitoring produce in grocery stores.

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Discrimination of herbicide-resistant kochia with hyperspectral imaging

Cited by 18 publications

References 24 publications

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Hyperspectral imaging and neural networks to classify herbicide-resistant weeds

Hyperspectral imaging and machine learning for monitoring produce ripeness

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