Hyperspectral imaging and neural networks to classify herbicide-resistant weeds

Bryan Scherrer,Prashant Jha,Joseph A Shaw,John Sheppard

doi:10.1117/1.jrs.13.044516

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.

Highlights

Weed control is a major problem in worldwide agriculture, costing an estimated $25.2 billion a year to control.[1]
To achieve more real-time classification and detection capabilities, we have been developing remote sensing methods based on hyperspectral imaging and machine learning to classify different biotypes of herbicide-resistant weeds
This section details the classification of sugar beet and weed biotypes using the neural network algorithm described in Sec. 3 applied to spectra obtained from hyperspectral images recorded at the Southern Agricultural Research Center (SARC) in Huntley, Montana

Summary

Introduction

Weed control is a major problem in worldwide agriculture, costing an estimated $25.2 billion a year to control.[1]. The process involves a detailed spectral analysis of different weeds (e.g., kochia, marestail, and common lambsquarters) in different crops (e.g., wheat, barley, and sugar beet), aimed at differentiating between herbicide-resistant and -susceptible biotypes of the weed Other work in this field includes distinguishing between susceptible- and glyphosate-resistant Palmer amaranth biotypes,[14] differentiating between glyphosate-susceptible and glyphosate-resistant Italian ryegrass,[15] and detecting the injury on crops as a result of dicamba and glyphosate.[16]. The paper follows this format: Sec. 2 presents the methodology used to grow the plants, the species and biotypes imaged, and the imaging and calibration process; Sec. 3 details the machine learning algorithm used to discriminate between plant species and biotypes; Sec. 4 discusses the classification accuracies on different combinations of crops and weeds; Sec. 5 offers concluding remarks and proposals for further work

Physical Experiments and Data Collection

Image Processing

Neural Networks

Results and Analysis

Ground-Based Imaging Results

Conclusion