Luan P Pott1,2, Telmo Jc Amado3, Raí A Schwalbert1,2, Elodio Sebem4, Mithila Jugulam2, Ignacio A Ciampitti2. 1. Agricultural Engineering Department, Federal University of Santa Maria, Rural Science Centre, Santa Maria, Brazil. 2. Department of Agronomy, Kansas State University, Manhattan, Kansas, USA. 3. Soil Department, Federal University of Santa Maria, Rural Science Centre, Santa Maria, Brazil. 4. Technology in Geoprocessing, Federal University of Santa Maria, Polytechnic School, Santa Maria, Brazil.
Abstract
BACKGROUND: Site-specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non-targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non-targets for each VI using training and validation data sets. RESULTS: The main outcomes of this study for effectively distinguishing weeds from other non-targets are (i) training and validation data exhibited similar spectral curves, (ii) red and near-infrared spectral bands presented greater accuracy relative to the other bands, and (iii) the tested VIs increased the discrimination accuracy related to single bands, with an overall accuracy above 95% and a kappa above 0.93. CONCLUSION: This study provided a novel approach to distinguish weeds from other non-targets utilizing a ground-level sensor before cash crop planting based on field spectral data. However, the limitations of this study are related to the spatial resolution to distinguish weeds that might be closer to the one this study presented, and also related to the soil and crop residues conditions at the time of collecting the readings. Overall the results presented contribute to an improved understanding of spectral signatures from different targets (weeds, soils, and residues) before planting time supporting SSWM.
BACKGROUND: Site-specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non-targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non-targets for each VI using training and validation data sets. RESULTS: The main outcomes of this study for effectively distinguishing weeds from other non-targets are (i) training and validation data exhibited similar spectral curves, (ii) red and near-infrared spectral bands presented greater accuracy relative to the other bands, and (iii) the tested VIs increased the discrimination accuracy related to single bands, with an overall accuracy above 95% and a kappa above 0.93. CONCLUSION: This study provided a novel approach to distinguish weeds from other non-targets utilizing a ground-level sensor before cash crop planting based on field spectral data. However, the limitations of this study are related to the spatial resolution to distinguish weeds that might be closer to the one this study presented, and also related to the soil and crop residues conditions at the time of collecting the readings. Overall the results presented contribute to an improved understanding of spectral signatures from different targets (weeds, soils, and residues) before planting time supporting SSWM.
Authors: Francisco Garibaldi-Márquez; Gerardo Flores; Diego A Mercado-Ravell; Alfonso Ramírez-Pedraza; Luis M Valentín-Coronado Journal: Sensors (Basel) Date: 2022-04-14 Impact factor: 3.847
Authors: Brian L Beres; Jerry L Hatfield; John A Kirkegaard; Sanford D Eigenbrode; William L Pan; Romulo P Lollato; James R Hunt; Sheri Strydhorst; Kenton Porker; Drew Lyon; Joel Ransom; Jochum Wiersma Journal: Front Plant Sci Date: 2020-06-16 Impact factor: 5.753