USMA-BOF: A Novel Bag-of-Features Algorithm for Classification of Infected Plant Leaf Images in Precision Agriculture

Vijh, Surbhi; Gaurav, Prashant; Kumar, Sumit; Bansal, Priti; Singh, Mayank; Khan, Muhammad Attique; Palade, Vasile

doi:10.1109/mra.2023.3315929

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…To overcome this problem, a few researchers employed feature selection techniques that remove the irrelevant features from the final classification. In addition to that, the performance has been improved significantly after employing the optimization process [35,36]. Few latest studies also focused on deep learning for the agri-yield classification using remote sensing images [20,[37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

Albarakati,

Khan,

Hamza

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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AI-driven precision agriculture applications can benefit from the large data source that remote sensing provides, as it can gather agricultural monitoring data at various scales throughout the year. Numerous advantages for sustainable agricultural applications, including yield prediction, crop monitoring, and climate change adaptation, can be obtained from remote sensing and artificial intelligence. In this work, we proposed a fully automated Optimized Self-Attention Fused Convolutional Neural Network (CNN) architecture for land use and land cover classification using remote sensing (RS) data. A new contrast enhancement equation has been proposed and utilized in the proposed architecture for the data augmentation. After that, a fused Self-Attention CNN architecture was proposed. The proposed architecture initially consists of two custom models named IBNR-65 and Densenet-64. Both models have been designed based on the inverted bottleneck residual mechanism and Dense Blocks. After that, both models were fused using a depth-wise concatenation and append a Self-Attention layer for deep features extraction. After that, we trained the model and performed classification using neural network (NN) classifiers. The results obtained from the NN classifiers are insufficient; therefore, we implemented a Bayesian Optimization and fine-tuned the hyperparameters of NN. In addition, we proposed a Quantum Hippopotamus Optimization Algorithm for the best feature selection. The selected features are finally classified using fine-tuned NN classifiers and obtained improved accuracy of 98.20, 89.50, and 91.70%, and the highest precision rate is 98.23, recall is 98.20, and f1-score is 98.21 respectively, for SIRI-WHU, EuroSAT, and NWPU datasets. Moreover, a detailed ablation study was conducted, and the performance was compared with SOTA. The proposed architecture shows

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Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

Albarakati,

Khan,

Hamza

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…In recent years, Convolutional Neural Networks (CNNs) have been rapidly developed and achieved excellent results in various fields [23], [24], [25], [26], [27], [28], [29]. It can automatically learn multi-dimensional feature information with a distinguishing degree between crops and weeds from input images.…”

Section: Introductionmentioning

confidence: 99%

An Improved Transformer Network With Multi-Scale Convolution for Weed Identification in Sugarcane Field

Sun,

Zhang,

Zhou

et al. 2024

IEEE Access

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Automated weeding equipment is urgently needed to deal with weeds in farmlands in the context of the rapid development of Intelligent Agriculture. A system for accurately identifying crops and weeds in images is crucial component of automated weeding equipment. However, in the field environment, crops and weeds grow intertwined, and weeds are very similar to sugarcane leaves, making it difficult to accurately segment crops, weeds, and their boundaries from images. In this paper, we proposed a novel network that fully utilizes low-level semantic information to accurately segment crops and weeds in images, improving the accuracy of crop and weed segmentation while reducing the need for training weight parameters and improving speed in the prediction stage. Specifically, we made three important modifications for crop and weed identification. First, a Multi-scale Feature Extraction and Fusion module (MFEF) was designed to capture abundant low-level semantic feature information. Afterward, we introduce a Global Response Normalization (GRN) block to select more useful feature information. Finally, a series of residual attention transformer layers are designed to transmit the long-range dependency information extracted between layers. Numerous experimental results confirmed that our proposed network achieved excellent performance in segmenting sugarcane and weed images. Specifically, (1) the mean accuracy and, Mean Intersection of Union (MIoU) reached 96.97% and 94.13%, respectively, (2) the training parameters of the model have been reduced by more than 25%, improving the Frames Per Second (FPS) value of the prediction process, and (3) is also effective on the publicly available BoniRob Dataset, indicating that the proposed model has considerable generalization ability. This study provides an accurate weed identification map and has reference significance for subsequent systems as mechanical weeding equipment.

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Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

Nobel,

Afroj,

Kabir

et al. 2024

Artificial Intelligence in Agriculture

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USMA-BOF: A Novel Bag-of-Features Algorithm for Classification of Infected Plant Leaf Images in Precision Agriculture

Cited by 5 publications

References 26 publications

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

An Improved Transformer Network With Multi-Scale Convolution for Weed Identification in Sugarcane Field

Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

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