Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm

Abstract

The diagnosis of plant disease by computer vision using digital image processing methodology is a key for timely intervention and treatment of healthy agricultural procedure and to increase the yield by natural means. Timely addressal of these ailments can be the difference between the prevention and perishing of an ecosystem. To make the system more efficient and feasible we have proposed an algorithm called Enhanced Fusion Fractal Texture Analysis (EFFTA). The proposed method consists of Feature Fusion technique which combines SIFT-Scale Invariant Feature Transform and DWT-Discrete Wavelet Transform based SFTA-Segment Based Fractal Texture Analysis. Image as a whole can be detected by shape, texture and color. SIFT is used to detect the texture feature, it extracts the set of descriptors that is very useful in local texture recognition and it captures accurate key points for detecting the diseased area. Further extraction of texture is considered and that can be performed by WSFTA method. It adopts intra-class analysis and inter-class analysis. Extracted features trained using Back Propagation Neural Network. It improves and expands the success rate and accuracy of extraction also it provides higher precision and efficiency when compared to the other traditional methods.

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

“…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.…”

“…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.…”

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

“…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].…”