Differentiate Soybean Response to Off-Target Dicamba Damage Based on UAV Imagery and Machine Learning

Vieira, Caio Canella; Sarkar, Shagor; Tian, Fengkai; Zhou, Jing; Jarquín, Diego; Nguyen, Henry T.; Zhou, Jianfeng; Chen, Pengyin

doi:10.3390/rs14071618

Cited by 11 publications

(18 citation statements)

References 55 publications

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“…Our study indicated that RF had the highest classification accuracy in the three regions ( Table 5 , Table 6 and Table 7 ), which is in line with the findings of many classification studies of daytime UAV images [ 12 , 35 , 36 , 37 ]. The RF algorithm integrates multiple decision trees with good high-dimensional data processing capability and can effectively avoid noise interference.…”

Section: Discussionsupporting

confidence: 89%

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach

Zhang

Zhou

et al. 2023

Sensors

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Fine classification of urban nighttime lighting is a key prerequisite step for small-scale nighttime urban research. In order to fill the gap of high-resolution urban nighttime light image classification and recognition research, this paper is based on a small rotary-wing UAV platform, taking the nighttime static monocular tilted light images of communities near Meixi Lake in Changsha City as research data. Using an object-oriented classification method to fully extract the spectral, textural and geometric features of urban nighttime lights, we build four types of classification models based on random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN) and decision tree (DT), respectively, to finely extract five types of nighttime lights: window light, neon light, road reflective light, building reflective light and background. The main conclusions are as follows: (i) The equal division of the image into three regions according to the visual direction can alleviate the variable scale problem of monocular tilted images, and the multiresolution segmentation results combined with Canny edge detection are more suitable for urban nighttime lighting images; (ii) RF has the highest classification accuracy among the four classification algorithms, with an overall classification accuracy of 95.36% and a kappa coefficient of 0.9381 in the far view region, followed by SVM, KNN and DT as the worst; (iii) Among the fine classification results of urban light types, window light and background have the highest classification accuracy, with both UA and PA above 93% in the RF classification model, while road reflective light has the lowest accuracy; (iv) Among the selected classification features, the spectral features have the highest contribution rates, which are above 59% in all three regions, followed by the textural features and the geometric features with the smallest contribution rates. This paper demonstrates the feasibility of nighttime UAV static monocular tilt image data for fine classification of urban light types based on an object-oriented classification approach, provides data and technical support for small-scale urban nighttime research such as community building identification and nighttime human activity perception.

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Section: Discussionsupporting

confidence: 89%

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach

Zhang

Zhou

et al. 2023

Sensors

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“…Each plot consisted of a single 2.13 m long row spaced 0.76 m apart. The Lee Farm and Rhodes Farm have been exposed to prolonged and homogeneously distributed off-target dicamba damage since 2017, where significant yield losses due to off-target dicamba exposure have been reported between non-DT and DT soybean genotypes (Canella Vieira et al, 2022a;Chen et al, 2022;Canella Vieira et al, 2022b). Off-target dicamba exposure was a result of dicamba volatilization from nearby cropping systems consisting of DT soybean and cotton.…”

Section: Plant Materials and Data Collectionmentioning

confidence: 99%

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

et al. 2022

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The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.

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“…Conventional soybean breeding programs develop new varieties through extensive field trials and phenotypic and genotypic screening to select genotypes with high-yielding potential and desirable economic traits, including off-target dicamba tolerance [ 15 ]. However, the assessment of off-target dicamba damage requires breeders to check hundreds or thousands of genotypes across multiple environments (combination of location, field, and year), which is time-consuming and labor-intensive [ 16 ]. In addition, visual scores may not be accurate due to their subjective nature [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, the assessment of off-target dicamba damage requires breeders to check hundreds or thousands of genotypes across multiple environments (combination of location, field, and year), which is time-consuming and labor-intensive [ 16 ]. In addition, visual scores may not be accurate due to their subjective nature [ 16 ]. In recent years, unmanned aerial vehicle (UAV) imaging technology has been extensively used in agriculture for collecting the high spatiotemporal resolution imagery data of crops [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning

Tian

Vieira²,

Zhou

et al. 2023

Sensors

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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 programs have identified genetic resources that confer greater tolerance to off-target dicamba damage in soybeans. Efficient and high throughput phenotyping tools can facilitate the collection of a large number of accurate crop traits to improve the breeding efficiency. This study aimed to evaluate unmanned aerial vehicle (UAV) imagery and deep-learning-based data analytic methods to quantify off-target dicamba damage in genetically diverse soybean genotypes. In this research, a total of 463 soybean genotypes were planted in five different fields (different soil types) with prolonged exposure to off-target dicamba in 2020 and 2021. Crop damage due to off-target dicamba was assessed by breeders using a 1–5 scale with a 0.5 increment, which was further classified into three classes, i.e., susceptible (≥3.5), moderate (2.0 to 3.0), and tolerant (≤1.5). A UAV platform equipped with a red-green-blue (RGB) camera was used to collect images on the same days. Collected images were stitched to generate orthomosaic images for each field, and soybean plots were manually segmented from the orthomosaic images. Deep learning models, including dense convolutional neural network-121 (DenseNet121), residual neural network-50 (ResNet50), visual geometry group-16 (VGG16), and Depthwise Separable Convolutions (Xception), were developed to quantify crop damage levels. Results show that the DenseNet121 had the best performance in classifying damage with an accuracy of 82%. The 95% binomial proportion confidence interval showed a range of accuracy from 79% to 84% (p-value ≤ 0.01). In addition, no extreme misclassifications (i.e., misclassification between tolerant and susceptible soybeans) were observed. The results are promising since soybean breeding programs typically aim to identify those genotypes with ‘extreme’ phenotypes (e.g., the top 10% of highly tolerant genotypes). This study demonstrates that UAV imagery and deep learning have great potential to high-throughput quantify soybean damage due to off-target dicamba and improve the efficiency of crop breeding programs in selecting soybean genotypes with desired traits.

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Differentiate Soybean Response to Off-Target Dicamba Damage Based on UAV Imagery and Machine Learning

Cited by 11 publications

References 55 publications

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach

Identification of genomic regions associated with soybean responses to off-target dicamba exposure

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning

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