Hyperspectral imaging and neural networks to classify herbicide-resistant weeds

Abstract

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.

Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards

“…The high spectral resolution in hyperspectral images allow the detection of spectral details that can be imperceptible in multispectral images due to their discrete spectral nature [32]. UAV-based hyperspectral imaging in agriculture has been successfully used in chlorophyll [33], biomass [34], nitrogen [35] or water [36] content estimation; the detection of diseases [37]; weed classification [38]; the evaluation and classification of crop water status [39]; etc. Therefore, hyperspectral remote sensing technologies have improved our capability for understanding the processes of biophysical and biochemical properties of vegetation [40].…”

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

“…The neural network should also be used to more hyperspectral maps of herbicide-susceptible and -resistant weeds among crops to evaluate the model under various field settings that includes unknown weed species. In future study, these maps should include data from the field to explore the efficacy of weed detection by varying the crops, mixed pixels, weeds, lighting, imager distance from plants, and environmental conditions [121].…”

Reduced-cost hyperspectral convolutional neural networks

“…Typically, these methods are based on support vector machines (SVMs), 23,24 k-nearest-neighbors, 25 random forests, 26 or feedforward neural networks. 27 Because of the high dimensionality, other methods use a feature engineering step before training a classifier. 28 This can be done using feature extraction or feature selection approaches: the former apply linear or nonlinear transformations to extract specific features from the original data, 29,30 whereas the latter select the most useful individual features (i.e., spectral bands) of the data without transforming it.…”