Deep Learning-Based Model for Classification of Bean Nitrogen Status Using Digital Canopy Imaging

Baesso, Murilo Mesquita; Leveghin, Luisa; Sardinha, Edson José de Souza; Oliveira, Gabriel  Pagin de Carvalho Nun; Sousa, Rafael Vieira de

doi:10.1590/1809-4430-eng.agric.v43n2e20230068/2023

Cited by 4 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This research presents an enhanced U-Net algorithm to improve the accuracy of remote sensing image classification. This algorithm synergistically combines spatial attention and multi-scale features (Baesso et al, 2023). The proposed methodology chiefly employs dilated convolutions across diverse scales to enlarge the receptive field.…”

Section: Introductionmentioning

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.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

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.

View full text Add to dashboard Cite

show abstract

Classification model for chlorophyll content using CNN and aerial images

Wagimin,

Ismail,

Fauzi

et al. 2024

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Nitrogen monitoring and inversion algorithms of fruit trees based on spectral remote sensing: a deep review

Xi,

Gu,

Zhang

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Nitrogen, as one of the important elements affecting the growth and development of fruit trees, leads to slowed protein synthesis and reduced photosynthesis, resulting in yellowing of the leaves, poor tree growth, and decreased yield under nitrogen-deficient conditions. In order to minimize losses and maximize fruit yield, there is often an occurrence of excessive fertilization, soil structure degradation, and water pollution. Therefore, accurate and real-time monitoring of nitrogen content in fruit trees has become the fundamental prerequisite for precision management of orchards. Furthermore, precision orchard management is crucial for enhancing fruit quality by maintaining the optimal growth conditions necessary for trees. Moreover, it plays a vital role in safeguarding the ecological environment by mitigating the overuse of fertilizers and pesticides. With the continuous development and application of spectral remote sensing technology in agricultural monitoring and land management, this technology can provide an effective method for monitoring nitrogen content. Based on a review of relevant literature, this paper summarizes a research framework for monitoring and inversion of nitrogen content in fruit trees, which provides help for further research. Firstly, based on different remote sensing platforms, the application was discussed, on spectral remote sensing technology in the acquisition of nitrogen content in fruit trees. Secondly, the index parameters that can reflect the nitrogen content of fruit trees are summarized, which provides practical guidance for remote sensing monitoring. Additionally, the regression algorithms and application situations based on spectral data for nitrogen content were introduced. In conclusion, in response to the current issues and technological limitations, future research should focus on studying the nitrogen content characteristics of fruit trees during different phenological periods, integrating multi-type data information, and thereby improving the universality of the nitrogen content inversion model for fruit trees.

show abstract

Deep Learning-Based Model for Classification of Bean Nitrogen Status Using Digital Canopy Imaging

Cited by 4 publications

References 21 publications

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

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

Classification model for chlorophyll content using CNN and aerial images

Nitrogen monitoring and inversion algorithms of fruit trees based on spectral remote sensing: a deep review

Contact Info

Product

Resources

About