Novel sensor-based method (quick test) for the in-season rapid evaluation of herbicide efficacy under real field conditions in durum wheat

Abstract

Optimum herbicide use is a key factor affecting the success of any integrated weed management strategy. The main objective of the current study was to implement a method based on spectrometer measurements for the in situ evaluation of herbicide efficacy and the detection of potentially herbicide-resistant weeds. Field trials were conducted in Greece between 2018 and 2020 in several durum wheat fields (Triticum durum Desf.). In all trials, the overall effect of herbicide application on the recorded Normalized Difference Vegetation Index (NDVI) values (at 1 and 2 wk after treatment [WAT]) was significant (P ≤ 0.001). For the majority of the surveyed fields, low NDVI values were recorded after 2,4-D application and a mixture of clopyralid + florasulam from 1 WAT, suggesting their increased efficacy. In several cases, the application of pyroxsulam + florasulam resulted in significant NDVI reductions at 2 WAT. As observed at the end of the growing seasons, the herbicides that reduced NDVI resulted in lower weed biomass. Strong correlations were observed between weed aboveground biomass and NDVI (2 WAT). In particular, R2 values were 0.8234 to 0.8649, 0.8453, 0.8595, 0.8149, and 0.8925 for the Aliartos, Thiva, Domokos, Larissa, and Orestiada fields, respectively. The overall effects of herbicide application on wheat grain yield were also significant (P ≤ 0.001). Pot experiments confirmed that the high NDVI values in some cases could be attributed to the presence of herbicide-resistant weeds. For instance, the resistance indices of two accessions of catchweed bedstraw (Galium aparine L.) to mesosulfuron-methyl + iodosulfuron-methyl-sodium ranged between 9.7 and 13.2, whereas one sterile oat [Avena sterilis L. ssp. ludoviciana (Durieu) Gillet & Magne] accession was 8.8 times more resistant to fenoxaprop-p-ethyl than a susceptible one. The present study is targeted at making a significant contribution toward establishing cause–effect relationships and presenting a useful tool for developing more effective weed management practices in more arable crops and under different soil and climatic conditions.

Screening glyphosate-alternative weed control options in important perennial crops

“…Weed Science (P ≤ 0.001; n = 36; Y = −218.929 þ 1,318.02 * X; R = 0.987; R 2 = 0.975; RMSE = 33.088; Figure 2B). These correlations indicate that NDVI can be a reliable, nondestructive tool for weed control evaluation and agree with previous findings (Travlos et al 2021).…”

“…The correlation between C. bonariensis biomass and NDVI was strong and positive in the second evaluation (Eval 2) of 2018 to 2019 (P ≤ 0.001; n = 36; Y = −203.347 þ 910.336 * X; R = 0.894; R 2 = 0.799; RMSE = 82.823; Figure 3B) and 2019 to 2020 (P ≤ 0.001; n = 36; Y = −166.913 þ 769.842 * X; R = 0.908; R 2 = 0.825; RMSE = 64.731; Figure 3B). These results suggest that NDVI values were in accordance with those of weed biomass and agree with other studies in which this vegetation index was effectively used to estimate herbicide efficacy (Lewis et al 2014;Travlos et al 2021).…”

Estimating the Reduction in Cover Crop Vitality Followed by Pelargonic Acid Application Using Drone Imagery

“…Other than EXG, according to Henry et al ( 2004) and Lewis et al (2014), one of the common vegetative indices for detecting injury symptoms on plants caused by herbicides is NDVI [51]. In this experiment, on 1st_DAT in 2019, the application of PA treatments showed significantly more vitality loss in the visual observation (estimated vitality) than in the NDVI index.…”

Sustainable Crop and Weed Management in the Era of the EU Green Deal: A Survival Guide

“…Precision agriculture technologies have been extensively reported to reduce the greenhouse gas emissions and the external input of agricultural systems [61]. The use of cuttingedge sensors, the development of Decision Support Systems, and specific improvements in machinery have contributed to an increase in new technologies adoption for weed management in major field crops and a reduction in herbicide input [109,110]. The evolution of precision agriculture tools, such as the development of autonomous robotic weed-control machines, requires the proper detection of the weed and the application of precise weed control either mechanically or with chemicals to prevent possible crop injury, whereas the herbicide input will be reduced, and the crop yield will not be severely affected [111,112].…”