Crop Identification Based on Remote Sensing Data using Machine Learning Approaches for Fresno County, California

Suchi, Sunanda Das; Menon, Aparna; Malik, Ashima; Hu, Jie; Gao, Jerry

doi:10.1109/bigdataservice52369.2021.00019

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…According to Ref. [ 43 ], the highest classification accuracy achieved of 95% with a voting classifier ensemble in crop mapping in United States using Google earth Engine (GEE). Using cloud platforms, several high-resolution land cover/use maps at the global scale were recently produced [ 44 , 45 ].…”

Section: Discussionmentioning

confidence: 99%

Investigation on the use of ensemble learning and big data in crop identification

Ahmed¹,

Mahmoud²,

Farg³

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Investigation on the use of ensemble learning and big data in crop identification

Ahmed¹,

Mahmoud²,

Farg³

et al. 2023

Heliyon

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“…The major concept is to reproduce the human vision for dealing with big data issues, utilize every data accessible, and offer semantic data as the output. Several techniques, models, and benchmark datasets of reference images are obtainable in the image classification field ( Suchi et al, 2021 ). Recently, many researchers have utilized the DL method for processing RSI.…”

Section: Introductionmentioning

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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“…DL, referring to a deep neural network, is a type of ML technique, and because of its data expression and dominant feature extraction capability, it has been widely adopted. Over the years, the identification rate of DL on most classical identification processes has enhanced considerably [9]. Numerous studies have exhibited that DL can extract features from RS imagery and enhance the classifier performance.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification

Almasoud,

Mengash,

Saeed

et al. 2023

Biomimetics

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Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques.

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Crop Identification Based on Remote Sensing Data using Machine Learning Approaches for Fresno County, California

Cited by 10 publications

References 16 publications

Investigation on the use of ensemble learning and big data in crop identification

Investigation on the use of ensemble learning and big data in crop identification

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

Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification

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