Automatic Detection and Classification of Tomato Pests Using Support Vector Machine Based on HOG and LBP Feature Extraction Technique

Pattnaik, Gayatri; Parvathi, K.

doi:10.1007/978-981-15-6353-9_5

Cited by 20 publications

(5 citation statements)

References 13 publications

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“…The average accuracy measure obtained as 83% for both the classes. Pattnaik and Parvathi (2021) [14] proposed a ML approach based on hand selected feature extraction followed by support vector machine (SVM) based classification for aphids. Both the histogram of oriented gradient (HOG) and local binary pattern (LBP) features were used for training the SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of whitefly pests in crops employing image enhancement and machine learning

2023

IJATEE

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Agricultural research is currently undergoing a transformation with the emergence of precision agriculture, which utilizes automated monitoring, data collection, and analysis technologies. This new paradigm is expected to have a profound impact on agricultural practices, aiming to significantly improve both the quantity and quality of crop yields. One crucial challenge in precision agriculture is the automated detection of pests, as they can cause substantial damage to agricultural produce. However, the diverse nature of pests and the variety of crops they attack pose significant challenges for automated pest detection. A deep neural network-based approach has been proposed for the automated detection of whitefly pests in common plants. Before the actual training process, the captured images are subjected to contrast enhancement to ensure uniformity, as they are typically taken under varying lighting and partial shading conditions. The preprocessing step has been shown to enhance the accuracy of the proposed method by making the system more resilient to image degradations. The techniques utilized in this research employ decision tree (DT), convolutional neural networks (CNN), residual networks (ResNet), and attention-based CNN. The experimental results indicate that the proposed technique achieves accuracy rates of 81%, 96%, 97.5%, and 98% for the four models, namely DT, CNN, ResNet, and attention-based CNN, respectively. By comparing the results with those of baseline contemporary techniques, it is evident that the proposed model outperforms other deep learning models in terms of classification accuracy. Consequently, the method presented in this study can be considered an effective automated technique for accurately detecting whitefly pests and identifying pest infestations in crops.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of whitefly pests in crops employing image enhancement and machine learning

2023

IJATEE

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“…HOG is a very powerful descriptor proposed by Dalal and Triggs in 2005, which was initially developed for human detection [73]. However, later it is extended and applied to other topics of computer vision problems including facial recognition [74], gender and age estimation [75], detection of plant pathology's [76] and recognition of facial expressions [77]. HOG describes the Gradient values (G h , G y ) are computed for each pixel using a centered 1 − D derivative filter, in the horizontal and vertical directions.…”

Section: Histogram Of Oriented Gradient (Hog)mentioning

confidence: 99%

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Neggaz

Fizazi

2021

Preprint

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Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to the technological progress and the increase of mobile applications. HFA explores several issues as gender recognition, facial expression, age, and race recognition for automatically understanding social life. In addition, the development of several algorithms inspired by swarm intelligence, biological inspiration, and physical/mathematical rules allow giving another dimension of feature selection in the field of machine learning and computer vision. This paper develops a novel wrapper feature selection method for gender recognition using the Archimedes optimization algorithm (AOA). The paper's primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several sub-regions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram oriented gradient (HOG), or Grey level co-occurrence matrix (GLCM). The proposed method (AOA) is assessed on two publicly datasets: Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The experimental results show a good performance of AOA compared to other recent and competitive optimizers as Sine cosine algorithm (SCA), Henry Gas Solubility Optimization (HGSO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Harris Hawks Optimize (HHO), Multi-verse Optimizer (MVO) and Manta-ray Foraging Optimizer (MRFO) in terms of accuracy and the number of the selected area.

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“…Research on deep learning in pest detection can be categorized into two approaches: pest detection and classification and pest-induced leaf infestation feature detection. The research on pest detection and classification mainly focuses on improving the classical deep learning methods, such as P. Venk et al [10], which achieved good results on pest datasets of three peanut crops by integrating VIT, PCA, and MFO; Pattnaik G et al [11], which feature extraction of pests by HOG and LBP, and the extracted feature maps are fed into SVM [12] classifiers for training; In contrast, leaf damage caused by insect pests can be detected in two ways: by quantifying the extent of insect damage to the leaf and by detecting the location of the insect-damaged leaf. For example, Liang et al [13] developed polynomial and logistic regression models for leaf extraction to estimate leaf damage; Da Silva et al [14] used image segmentation to preserve the leaf region, augmented the dataset with a synthesis technique, and trained the network with a model for detecting pest-induced damage to leaves; Fang et al [15], Zhu R et al [16], Zhu L et al [17], and others used the improved YOLO series of models to identify pest-induced leaf damage and achieved good detection results.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Tomato Pest Detection via Leaf Imagery with a New Loss Function

Mo,

Xie,

et al. 2024

Agronomy

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Pests have caused significant losses to agriculture, greatly increasing the detection of pests in the planting process and the cost of pest management in the early stages. At this time, advances in computer vision and deep learning for the detection of pests appearing in the crop open the door to the application of target detection algorithms that can greatly improve the efficiency of tomato pest detection and play an important technical role in the realization of the intelligent planting of tomatoes. However, in the natural environment, tomato leaf pests are small in size, large in similarity, and large in environmental variability, and this type of situation can lead to greater detection difficulty. Aiming at the above problems, a network target detection model based on deep learning, YOLONDD, is proposed in this paper. Designing a new loss function, NMIoU (Normalized Wasserstein Distance with Mean Pairwise Distance Intersection over Union), which improves the ability of anomaly processing, improves the model’s ability to detect and identify objects of different scales, and improves the robustness to scale changes; Adding a Dynamic head (DyHead) with an attention mechanism will improve the detection ability of targets at different scales, reduce the number of computations and parameters, improve the accuracy of target detection, enhance the overall performance of the model, and accelerate the training process. Adding decoupled head to Head can effectively reduce the number of parameters and computational complexity and enhance the model’s generalization ability and robustness. The experimental results show that the average accuracy of YOLONDD can reach 90.1%, which is 3.33% higher than the original YOLOv5 algorithm and is better than SSD, Faster R-CNN, YOLOv7, YOLOv8, RetinaNet, and other target detection networks, and it can be more efficiently and accurately utilized in tomato leaf pest detection.

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Automatic Detection and Classification of Tomato Pests Using Support Vector Machine Based on HOG and LBP Feature Extraction Technique

Cited by 20 publications

References 13 publications

Detection of whitefly pests in crops employing image enhancement and machine learning

Detection of whitefly pests in crops employing image enhancement and machine learning

An Intelligent Feature Selection using Archimedes Optimization algorithm for Facial Analysis

Enhanced Tomato Pest Detection via Leaf Imagery with a New Loss Function

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