Optimal deep convolutional neural network based crop classification model on multispectral remote sensing images

Chamundeeswari, G.; Srinivasan, S.; Bharathi, S.; Priya, P.; Kannammal, G. Rajendra; Rajendran, Sasikumar

doi:10.1016/j.micpro.2022.104626

Cited by 17 publications

(4 citation statements)

References 15 publications

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“…The name of Cluster #6 was the artificial neural network (ANN). Without complete information and even any prior knowledge, ANN models can still identify nonlinear relationships and predict dependent response, and thus were applied in food image analysis, quality detection, food safety risk prediction, crop distribution, and yield prediction, and various thermal and non-thermal food-processing operations [ 173 , 177 , 179 , 182 , 188 , 191 , 197 ]. Geng et al (2017) introduced a predictive model based on AHP integrated extreme learning machine (ELM), rather than a traditional artificial neural network (ANN), to monitor the food safety system in China [ 193 ].…”

Section: Abstract and Hot Spotsmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

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Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

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Section: Abstract and Hot Spotsmentioning

confidence: 99%

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Liu

Wang

Zhang

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…Achieved an even higher accuracy of 96%, indicating its potential for real-world deployment on mobile devices. Chamundeeswari et al (2022) projected an optimum DCNN-based crop classification model (ODCNN-CCM) utilizing multispectral RSIs. This introduced ODCNN-CCM method primarily utilizes an adaptive wiener filter-based image preprocessing method.…”

Section: Literature Reviewmentioning

confidence: 99%

Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis

Alotaibi,

Rajendran,

Rani K.

et al. 2024

PeerJ Computer Science

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Problem With the rapid advancement of remote sensing technology is that the need for efficient and accurate crop classification methods has become increasingly important. This is due to the ever-growing demand for food security and environmental monitoring. Traditional crop classification methods have limitations in terms of accuracy and scalability, especially when dealing with large datasets of high-resolution remote sensing images. This study aims to develop a novel crop classification technique, named Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC) for analyzing remote sensing images. The objective is to achieve high classification accuracy for various food crops. Methods The proposed DTODCNN-CC approach consists of the following key components. Deep convolutional neural network (DCNN) a GoogleNet architecture is employed to extract robust feature vectors from the remote sensing images. The Dipper throated optimization (DTO) optimizer is used for hyper parameter tuning of the GoogleNet model to achieve optimal feature extraction performance. Extreme Learning Machine (ELM): This machine learning algorithm is utilized for the classification of different food crops based on the extracted features. The modified sine cosine algorithm (MSCA) optimization technique is used to fine-tune the parameters of ELM for improved classification accuracy. Results Extensive experimental analyses are conducted to evaluate the performance of the proposed DTODCNN-CC approach. The results demonstrate that DTODCNN-CC can achieve significantly higher crop classification accuracy compared to other state-of-the-art deep learning methods. Conclusion The proposed DTODCNN-CC technique provides a promising solution for efficient and accurate crop classification using remote sensing images. This approach has the potential to be a valuable tool for various applications in agriculture, food security, and environmental monitoring.

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“…Five ASPP modules, four SAMs, and five residual modules make up the total architecture. Each convolutional layer in the backbone network is combined with a batch normalisation layer and a ReLU layer (Chamundeeswari et al, 2022). A ReLU layer in the ASPP module is placed after the atrous convolution and is identified by its pooling size of 2×2, convolution kernel size of 2×2, and transposed convolution stride of 2×2.…”

Section: Aspp-sam-unetmentioning

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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Optimal deep convolutional neural network based crop classification model on multispectral remote sensing images

Cited by 17 publications

References 15 publications

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis

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

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