Crop classification based on the spectrotemporal signature derived from vegetation indices and accumulated temperature

Zhang, Lifu; Gao, Liaoran; Huang, Changping; Wang, Nan; Wang, Sa; Peng, Man; Zhang, Xia; Tong, Qingxi

doi:10.1080/17538947.2022.2036832

Cited by 14 publications

(11 citation statements)

References 72 publications

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“…AT is an important index for crop growth and development. In agricultural modeling applications, AT data can be used to accurately predict the rate of plant growth and development, thereby reducing the influence of temperature variations and improving the models predictive performance [ 29 ]. Different rice cultivars have different resistance to BB, and the disease severity of rice may be different at different growth stages.…”

Section: Discussionmentioning

confidence: 99%

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Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data

Bai

Fang

et al. 2023

Plant Phenomics

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Bacterial blight poses a threat to rice production and food security, which can be controlled through large-scale breeding efforts toward resistant cultivars. Unmanned aerial vehicle (UAV) remote sensing provides an alternative means for the infield phenotype evaluation of crop disease resistance to relatively time-consuming and laborious traditional methods. However, the quality of data acquired by UAV can be affected by several factors such as weather, crop growth period, and geographical location, which can limit their utility for the detection of crop disease and resistant phenotypes. Therefore, a more effective use of UAV data for crop disease phenotype analysis is required. In this paper, we used time series UAV remote sensing data together with accumulated temperature data to train the rice bacterial blight severity evaluation model. The best results obtained with the predictive model showed an R p 2 of 0.86 with an RMSE p of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.

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Section: Discussionmentioning

confidence: 99%

“…It is believed that when a plant develops to a certain stage, the temperature it needs is relatively consistent [ 28 ]. AT can reflect the crop’s maturity, which is a good indicator of plant growth [ 29 ]. Therefore, AT data are introduced to be combined with UAV remote sensing data to evaluate the severity of BB.…”

Section: Introductionmentioning

confidence: 99%

Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data

Bai

Fang

et al. 2023

Plant Phenomics

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“…AAT is one of the most important environmental factors affecting flowering in plants. AAT plays an important role in agricultural research and impacts vegetation growth [ 46 ]. Due to the limitation of meteorological station distribution, most researchers investigated the flowering time irrespective of AAT effects.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide association study uncovers major genetic loci associated with flowering time in response to active accumulated temperature in wild soybean population

Yang

Li²,

Fan³

et al. 2022

BMC Genomics

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Flowering time and active accumulated temperature (AAT) are two key factors that limit the expanded production especially for soybean across different regions. Wild soybean provides an important germplasm for functional genomics study in cultivar soybean. However, the studies on genetic basis underlying flowering time in response to AAT especially in wild soybean were rarely reported. In this study, we used 294 wild soybean accessions derived from major soybean production region characterized by different AAT in Northeast of China. Based on genome-wide association study (GWAS), we identified 96 SNPs corresponded to 342 candidate genes that significantly associated with flowering time recorded in two-year experiments. Gene Ontology enrichment analysis suggests that the pathways of photosynthesis light reaction and actin filament binding were significantly enriched. We found three lead SNPs with -log10(p-value) > 32 across the two-year experiments, i.e., Chr02:9490318, Chr04:8545910 and Chr09:49553555. Linkage disequilibrium block analysis shows 28 candidate genes within the genomic region centered on the lead SNPs. Among them, expression levels of three genes (aspartic peptidase 1, serine/threonine-protein kinase and protein SCAR2-like) were significantly differed between two subgroups possessing contrasting flowering time distributed at chromosome 2, 4 and 9, respectively. There are 6, 7 and 3 haplotypes classified on the coding regions of the three genes, respectively. Collectively, accessions with late flowering time phenotype are typically derived from AAT zone 1, which is associated with the haplotypic distribution and expression levels of the three genes. This study provides an insight into a potential mechanism responsible for flowering time in response to AAT in wild soybean, which could promote the understanding of genetic basis for other major crops.

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“…The use of remote sensing technology to obtain semantic and spatial information on remote sensing pictures, where land item types are many and intricately distributed, describes a way to enhance land object identification competency. The SAM borrows from the concept of CBAM (Zhang et al, 2022). To secure the spatial feature weight map 𝑊 , the SAM initially makes use of the feature map of the spatial information pathway ( 𝑋 ).…”

Section: Sammentioning

confidence: 99%

Enhanced U-Net Algorithm for Typical Crop Classification Using Gf-6 WFV Remote Sensing Images

Jia,

Lan,

Jia

et al. 2024

Eng. Agríc.

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Accurate crop classification, crucial for a macro-level understanding of food production, formulating relevant agricultural policies, and predicting comprehensive agricultural productivity, enables precise crop distribution. In remote sensing image classification, feature selection and representation play a pivotal role in accuracy. An augmented U-Net algorithm, named ASPP-SAM-UNet, integrating spatial attention mechanisms and multiscale features is proposed for the enhancement of typical crop classification accuracy in remote sensing. The ASPP-SAM-UNet design integrates features over multiple scales, boosts the representational capacity of shallow features, and expands the neural network's receptive field by incorporating Atrous Spatial Pyramid Pooling (ASPP) into the convolutional components of the standard U-Net encoder via residual connections. The integration of the residual module allows for a profound fusion of deep and shallow features, thereby enhancing their utility. The spatial attention mechanism amalgamates spatial and semantic information, empowering the decoder to reclaim more spatial information. This study focused on Bayan County, Harbin City, Heilongjiang Province, China, employing GF-6 WFV remote sensing images for crop classification. Empirical outcomes showed a significant improvement in classification accuracy with the advanced algorithm, boosting the overall accuracy (OA) from 89.49 to 92.80%. Specifically, the segmentation accuracy for maize, rice, and soybean increased from 89. 90, 89.96, and 87.37% to 93.47, 94.82, and 89.35%, respectively. The suggested algorithm offers a pioneering performance standard for crop classification leveraging GF-6 WFV remote sensing imagery.

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Crop classification based on the spectrotemporal signature derived from vegetation indices and accumulated temperature

Cited by 14 publications

References 72 publications

Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data

Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data

Genome-wide association study uncovers major genetic loci associated with flowering time in response to active accumulated temperature in wild soybean population

Enhanced U-Net Algorithm for Typical Crop Classification Using Gf-6 WFV Remote Sensing Images

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