Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Tian, Fengkai, Vieira, Caio Canella, Zhou, Jing, Zhou, Jianfeng, Chen, Pengyin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056018/
https://www.ncbi.nlm.nih.gov/pubmed/36991952
http://dx.doi.org/10.3390/s23063241
_version_ 1785016023215243264
author Tian, Fengkai
Vieira, Caio Canella
Zhou, Jing
Zhou, Jianfeng
Chen, Pengyin
author_facet Tian, Fengkai
Vieira, Caio Canella
Zhou, Jing
Zhou, Jianfeng
Chen, Pengyin
author_sort Tian, Fengkai
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10056018
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-100560182023-03-30 Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning Tian, Fengkai Vieira, Caio Canella Zhou, Jing Zhou, Jianfeng Chen, Pengyin Sensors (Basel) Article 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. MDPI 2023-03-19 /pmc/articles/PMC10056018/ /pubmed/36991952 http://dx.doi.org/10.3390/s23063241 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tian, Fengkai
Vieira, Caio Canella
Zhou, Jing
Zhou, Jianfeng
Chen, Pengyin
Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning
title Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning
title_full Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning
title_fullStr Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning
title_full_unstemmed Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning
title_short Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning
title_sort estimation of off-target dicamba damage on soybean using uav imagery and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056018/
https://www.ncbi.nlm.nih.gov/pubmed/36991952
http://dx.doi.org/10.3390/s23063241
work_keys_str_mv AT tianfengkai estimationofofftargetdicambadamageonsoybeanusinguavimageryanddeeplearning
AT vieiracaiocanella estimationofofftargetdicambadamageonsoybeanusinguavimageryanddeeplearning
AT zhoujing estimationofofftargetdicambadamageonsoybeanusinguavimageryanddeeplearning
AT zhoujianfeng estimationofofftargetdicambadamageonsoybeanusinguavimageryanddeeplearning
AT chenpengyin estimationofofftargetdicambadamageonsoybeanusinguavimageryanddeeplearning