Banana leaf disease detection using K-means clustering and Feature extraction techniques

2020 ◽  
Author(s):  
Vandana Chaudhari ◽  
Manoj Patil
Keyword(s):  
Feature Extraction ◽  
Disease Detection ◽  
Extraction Techniques ◽  
Leaf Disease ◽  
Banana Leaf

Analysis of Plant Disease Detection and Classification Models: A Computer Vision Perspective

2020 ◽  
Vol 17 (12) ◽  
pp. 5422-5428
Author(s):  
K. Jayaprakash ◽  
S. P. Balamurugan
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Plant Disease ◽  
Weather Conditions ◽  
Vital Role ◽  
Plant Diseases ◽  
Disease Detection ◽  
Classification Models ◽  
Extraction Techniques ◽  
Identification Process

Presently, rapid and precise disease identification process plays a vital role to increase agricultural productivity in a sustainable manner. Conventionally, human experts identify the existence of anomaly in plants occurred due to disease, pest, nutrient deficient, weather conditions. Since manual diagnosis process is a tedious and time consuming task, computer vision approaches have begun to automatically detect and classify the plant diseases. The general image processing tasks involved in plant disease detection are preprocessing, segmentation, feature extraction and classification. This paper performs a review of computer vision based plant disease detection and classification techniques. The existing plant disease detection approaches including segmentation and feature extraction techniques have been reviewed. Additionally, a brief survey of machine learning (ML) and deep learning (DL) models to identify plant diseases also takes place. Furthermore, a set of recently developed DL based tomato plant leaf disease detection and classification models are surveyed under diverse aspects. To further understand the reviewed methodologies, a detailed comparative study also takes place to recognize the unique characteristics of the reviewed models.

scholarly journals An automated segmentation and classification model for banana leaf disease detection

2022 ◽  
Author(s):  
Krishnan V. Gokula ◽  
Deepa J. ◽  
Rao Pinagadi Venkateswara ◽  
Divya V. ◽  
Kaviarasan S.
Keyword(s):  
Classification Model ◽  
Disease Detection ◽  
Automated Segmentation ◽  
Leaf Disease ◽  
Banana Leaf

scholarly journals DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 474
Author(s):  
Jun Wang ◽  
Liya Yu ◽  
Jing Yang ◽  
Hao Dong
Keyword(s):  
Feature Extraction ◽  
Target Detection ◽  
Attention Mechanism ◽  
Detection Methods ◽  
Disease Detection ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Detection Algorithms ◽  
Leaf Disease ◽  
Image Dataset

In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

scholarly journals Leaf Spot Attention Networks Based on Spot Feature Encoding for Leaf Disease Identification and Detection

2021 ◽  
Vol 11 (17) ◽  
pp. 7960
Author(s):  
Chang-Hwan Son
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Leaf Spot ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Disease Detection ◽  
Discriminative Power ◽  
Leaf Disease ◽  
Feature Encoding ◽  
Feature Extractor

This study proposes a new attention-enhanced YOLO model that incorporates a leaf spot attention mechanism based on regions-of-interest (ROI) feature extraction into the YOLO framework for leaf disease detection. Inspired by a previous study, which revealed that leaf spot attention based on the ROI-aware feature extraction can improve leaf disease recognition accuracy significantly and outperform state-of-the-art deep learning models, this study extends the leaf spot attention model to leaf disease detection. The primary idea is that spot areas indicating leaf diseases appear only in leaves, whereas the background area does not contain useful information regarding leaf diseases. To increase the discriminative power of the feature extractor that is required in the object detection framework, it is essential to extract informative and discriminative features from the spot and leaf areas. To realize this, a new ROI-aware feature extractor, that is, a spot feature extractor was designed. To divide the leaf image into spot, leaf, and background areas, the leaf segmentation module was first pretrained, and then spot feature encoding was applied to encode spot information. Next, the ROI-aware feature extractor was connected to an ROI-aware feature fusion layer to model the leaf spot attention mechanism, and to be joined with the YOLO detection subnetwork. The experimental results confirm that the proposed ROI-aware feature extractor can improve leaf disease detection by boosting the discriminative power of the spot features. In addition, the proposed attention-enhanced YOLO model outperforms conventional state-of-the-art object detection models.

scholarly journals Identification of Leaf Disease Using Image Processing Technique

2021 ◽  
pp. 881-885
Author(s):  
Ankit Wagh ◽  
Ritwik Chumble ◽  
Santosh Kangane ◽  
Pranav Bakare
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Plant Disease ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Disease Detection ◽  
Revolutionary Change ◽  
Leaf Disease ◽  
Important Time

The increased availability of smartphones has made it easier for taking technology to every individual. Technology can play important role in the field of agriculture to improve outcomes. Plant disease is one of the important reasons for the decrement of yield. Bridging technology with agriculture can give revolutionary change. It is challenging to monitor the disease of plants manually. It consumes important time, resources, and need efforts. Hence faster image processing technique is used as a solution for it. Disease detection using image processing involves multiple steps like the acquisition of images, pre-processing, segmentation, feature extraction, and classification. This paper discusses a faster image processing technique using the KNN method. This algorithm gives an asymptotically faster method for image processing.

scholarly journals Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves

2021 ◽  
Vol 35 (4) ◽  
pp. 331-339
Author(s):  
Wiharto ◽  
Fikri Hashfi Nashrullah ◽  
Esti Suryani ◽  
Umi Salamah ◽  
Nurcahya Pradana Taufik Prakisy ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Gabor Filters ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Support Vector ◽  
Disease Detection ◽  
Final Decision ◽  
Early Detection Of Disease ◽  
Leaf Disease

The disease in tomato plants, especially on tomato leaves will have an impact on the quality and quantity of tomatoes produced. Handling disease on tomato leaves that must be done is to detect the type of disease as early as possible, then determine the treatment that must be done. Detection of its types of tomato plant diseases requires sufficient knowledge and experience. The problem is that many beginner farmers in growing tomatoes do not have much knowledge, so they have failed in growing tomatoes. Based on these cases, this study proposes a model for the early detection of disease in tomato leaves based on image processing. The research method used is divided into 5 stages, namely preprocessing, segmentation, feature extraction, classification, and performance evaluation. The feature extraction stage used is texture-based with Gabor filters and color-based filters. The final decision is determined by the Support Vector Machine (SVM) classification algorithm with the Radial Basis Function (RBF) kernel. The test results of the tomato leaf disease detection system produced an average performance parameter of 98.83% specificity, 90.37% sensitivity, 90.34% F1-score, 90.37% accuracy, and 94.60% area under the curve (AUC). Referring to the resulting of the AUC performance, the tomato leaf disease detection system is in the very good category.

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