2023
DOI: 10.3390/s23063241
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Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning

Abstract: Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crops, such as soybean and cotton, which has resulted in severe off-target dicamba exposure and substantial yield losses to non-tolerant crops. There is a strong demand for non-genetically engineered DT soybeans through conventional breeding selection. Public breeding … Show more

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Cited by 7 publications
(5 citation statements)
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“…It is noteworthy that Jones et al (2019) proved that dicamba can move up to 152 m from the application area, and this is something to seriously consider [31]. Tian et al (2023) proved that unmanned aerial vehicle (UAV) imagery and deep learning have great potential to accurately quantify soybean damage due to off-target dicamba and thus give the tools for a wide screening and selection of many soybean biotypes [33]. Marques et al [34] quantified dicamba injury on soybeans by means of a spectral vegetation index, the Triangular Greenness Index (TGI).…”
Section: Crop Referencementioning
confidence: 99%
“…It is noteworthy that Jones et al (2019) proved that dicamba can move up to 152 m from the application area, and this is something to seriously consider [31]. Tian et al (2023) proved that unmanned aerial vehicle (UAV) imagery and deep learning have great potential to accurately quantify soybean damage due to off-target dicamba and thus give the tools for a wide screening and selection of many soybean biotypes [33]. Marques et al [34] quantified dicamba injury on soybeans by means of a spectral vegetation index, the Triangular Greenness Index (TGI).…”
Section: Crop Referencementioning
confidence: 99%
“…YOLO v5 was employed to classify Palmer amaranth weed from soybeans (Barnhart et al, 2022). DenseNet 121 model effectively identified Dicamba-tolerant soybeans (Tian et al, 2023).…”
Section: Biotic and Abiotic Stressesmentioning
confidence: 99%
“…Weed detection is by far the most common application, with other uses like yield prediction and disease recognition also being explored (Table 3). Pest detection RGB Several CNNs 0.94 1 [58] Disease recognition RGB Several CNNs 0.99 1 [59] Herbicide damage RGB DenseNet121 0.82 1 [60] Maturity estimation RGB DCNN 3.00 6 [61] Crop mapping RGB, MS, VI U-Net 0.94 5 [62] Defoliation estimation RGB DefoNet CNN 0.91 1 [63] Maturity estimation RGB DS-SoybeanNet CNN 0.86-0.99 1 [64] Yield estimation RGB, MS DCNN 0.78 1 Legend: 1 Accuracy; 2 F1-score; 3 Correlation; 4 R 2 ; 5 Kappa coefficient; 6 RMSE; 7 IoU.…”
Section: Uav Images As Main Input Datamentioning
confidence: 99%
“…Although addressing these problems is not always straightforward, this is a requirement for the development of technologies that work under real conditions. One relatively straightforward way to increase variability is to gather data through multiple different years [59]. Class imbalance, on the other hand, can be counter-acted by a number of techniques that include data subsampling, data augmentation and class weighting [62].…”
Section: Uav Images As Main Input Datamentioning
confidence: 99%