Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm

Gangadharan, Kapilya; Rosline, G. Jeba; Dhanasekaran, D.; Kanagasabai, Malathi

doi:10.14569/ijacsa.2020.0110221

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…This type of method mainly uses techniques of image processing, such as filtering, threshold segmentation, morphological operations, etc., to enhance the contrast and separability of small targets, and then uses classic target detection algorithms, such as HOG, SIFT, SURF, etc., to extract the features of and locate small targets [1][2][3][4]. For example, Srivastava et al [5] proposed an improved hybrid morphological filter for detecting small targets.…”

Section: Methods Based On Traditional Image Processingmentioning

confidence: 99%

“…To compare the detection performance of TGC-YOLOv5 on large, medium, and small drones within the SUAV-DATA dataset, we divided the SUAV-DATA dataset into three parts: large targets (pixel size above 96 2 ), medium targets (pixel size between 32 2 and 96 2 ), and small targets (pixel size below 32 2 ). We trained and evaluated TGC-YOLOv5 on these three categorized drone datasets, and the experimental results are shown in Table 3.…”

Section: Comparison Of Detection Performance For Different Sizes Of D...mentioning

confidence: 99%

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TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

Zhao

Sun

et al. 2023

Drones

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Drone detection is a significant research topic due to the potential security threats posed by the misuse of drones in both civilian and military domains. However, traditional drone detection methods are challenged by the drastic scale changes and complex ambiguity during drone flight, and it is difficult to detect small target drones quickly and efficiently. We propose an information-enhanced model based on improved YOLOv5 (TGC-YOLOv5) for fast and accurate detection of small target drones in complex environments. The main contributions of this paper are as follows: First, the Transformer encoder module is incorporated into YOLOv5 to augment attention toward the regions of interest. Second, the Global Attention Mechanism (GAM) is embraced to mitigate information diffusion among distinct layers and amplify the global cross-dimensional interaction features. Finally, the Coordinate Attention Mechanism (CA) is incorporated into the bottleneck part of C3, enhancing the extraction capability of local information for small targets. To enhance and verify the robustness and generalization of the model, a small target drone dataset (SUAV-DATA) is constructed in all-weather, multi-scenario, and complex environments. The experimental results show that based on the SUAV-DATA dataset, the AP value of TGC-YOLOv5 reaches 0.848, which is 2.5% higher than the original YOLOv5, and the Recall value of TGC-YOLOv5 reaches 0.823, which is a 3.8% improvement over the original YOLOv5. The robustness of our proposed model is also verified on the Real-World open-source image dataset, achieving the best accuracy in light, fog, stain, and saturation pollution images. The findings and methods of this paper have important significance and value for improving the efficiency and precision of drone detection.

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Section: Methods Based On Traditional Image Processingmentioning

confidence: 99%

Section: Comparison Of Detection Performance For Different Sizes Of D...mentioning

confidence: 99%

TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

Zhao

Sun

et al. 2023

Drones

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“…A method to spot the diseased area on any part of the plant is proposed [7] Which used SIFT and WSFTA fusion method that extracts the required feature and for feature selection process it adapted PCA with KNN classifier algorithm which obtained an average accuracy of 95.9% which resulted in identifying various diseases on different plants and different parts of the plants. A color-based confirmation helps the Neural Network to differentiate between Pests or objects with similar characters like size and shape as the target pathogen using the color [8].…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of various pest attacks and infection on plants using recursive backpropagation neural network with GA based particle swarm optimization algorithm

Gangadharan

Kumari

Dhanasekaran

et al. 2020

IJEECS

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<span>Machine learning methodologies are commonly used in the field of precession farming. It prospects greatly in the plant safety measure like disease detection and classification of pest attacks. It highly influences the crop production and management. The venture of our system is to produce healthy plantation. The proposed system involves Enhanced Fractal Texture Feature Analysis and Machine Learning methodology for classification. Hence more than ever there is a need for such a tool that combines image processing methodologies and the Neural network concepts and that is supported by huge cloud of structured data which makes the diagnosis part much easier and convenient. The proposed system recognizes and classifies the plant taxonomy and the infection also it measures the percentage of infection. The neural network concept followed in our proposed system is focused on Artificial Neural Network which uses Recursive Back Propagation Neural network to speed up the training process and the weights on ANN is optimized using Genetic Algorithm based Particle Swarm Optimization technique. We have used MATLAB to implement the concept and obtained better accuracy in disease detection and proved to be an efficient method.</span>

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