Pre‐planting weed detection based on ground field spectral data

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. © 2019 Society of Chemical Industry

Assessment of Weed Classification Using Hyperspectral Reflectance and Optimal Multispectral UAV Imagery

“…The accuracy of the classification for the weed discrimination using LDA and support vector machine (SVM) has been increased at 15 bands compared to the eight bands at more than 90% and 85%, respectively [24]. Similar results were found for the separation between weed species using LDA, which has a high degree of separation [25]. A previous study found that discriminant analysis (DA) can detect grass and broadleaf weed species with 70 to 100% accuracy [26].…”

“…Additionally, the different growth stages between week two and week four were not prominent. Weed discrimination with other species was investigated during pre-planting and found the overall accuracy above 95% [25]. It is of excellent significance to increase weed identification accuracy for pre-planting applications.…”

Toward a Better Understanding of Genotype × Environment × Management Interactions—A Global Wheat Initiative Agronomic Research Strategy

“…For instance, research papers have exploited the variation in productive capacity for wheat across a field and integrated it with growing season assessments of crop growth for improved nitrogen management (e.g., Schwalbert et al, 2019). Likewise, weed and disease monitoring with remote sensing is an emerging area of crop management (e.g., Franke and Menz, 2007;Cruppe et al, 2017;Pott et al, 2019). Farmers have variable rate seed and fertilizer equipment and often access local weather stations.…”

Assessing Hyperspectral Vegetation Indices Responses of Six Pigweed Species

“…Hyperspectral light reflectance data acquired from plant leaves and canopies have shown promise for differentiating between plants. Furthermore, vegetation indices derived with hyperspectral reflectance data have enhanced plant mapping and separation of plants from other targets [12] [13] [14].…”

“…In a pre-plant weed detection study, seven hyperspectral vegetation indices were more effective than single-band data in separating weeds from nontargets such as residue and bare soil [14]. Compared with broadband vegetation indices, hyperspectral narrowband vegetation indices have been documented to improve estimates of vegetation and agricultural crops biophysical and biochemical parameters [15] [17] [19].…”