scholarly journals Agricultural Plant Leaf Disease Detection and Diagnosis Using Image Processing Based on Morphological Feature Extraction

2014 ◽  
Vol 4 (5) ◽  
pp. 24-30 ◽  
Author(s):  
Mr. Sachin B. Jagtap ◽  
◽  
Mr. Shailesh M. Hambarde
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Morphological Feature ◽  
Disease Detection ◽  
Plant Leaf ◽  
Agricultural Plant ◽  
Leaf Disease ◽  
Detection And Diagnosis

scholarly journals DETECTION OF PLANT LEAF DISEASES IN AGRICULTURE USING RECENT IMAGE PROCESSING TECHNIQUES – A REVIEW

2019 ◽  
Vol 7 (1) ◽  
pp. 29-33
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
State Of The Art ◽  
Disease Classification ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Processing Techniques ◽  
Large Work ◽  
Respective Product ◽  
Automatic Technique

Purpose: Agricultural productivity is something on which the economy highly depends in India as well in all over the world. India is an agriculture-dependent country; wherein about 70% of the population depends on agriculture. Methodology: This is one of the main reasons that disease detection in agriculture plays an important role, as having the disease in plant leaf is quite natural. If proper observations are not taken in the agriculture field then it causes serious effects on plants due to which respective product quality and productivity are affected. Detection of plant leaf disease through effective and accurate automatic technique is beneficial at the starting stage as it reduces a large work of monitoring in big farms of crops. Result: This paper presents the review on the state of the art disease classification techniques presently used using image processing that can be used for plant leaf disease detection in agriculture.

Plant Leaf Disease Detection Using Image Processing

2021 ◽  
pp. 161-168
Author(s):  
M. Sahana ◽  
H. Reshma ◽  
R. Pavithra ◽  
B. S. Kavya
Keyword(s):  
Image Processing ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease

scholarly journals Covariance Kalman Geometric Graph Based Feature Extraction And Bernoulli Kernel Classifier For Plant Leaf Disease Prediction

2021 ◽  
Vol 12 (3) ◽  
pp. 4904-4917
Author(s):  
Mohammed Zabeeulla A N Et. al.
Keyword(s):  
Feature Extraction ◽  
Agricultural Land ◽  
Geometric Graph ◽  
Plant Diseases ◽  
Classification Error ◽  
Infected Leaf ◽  
Disease Prediction ◽  
Plant Leaf ◽  
Agricultural Plant ◽  
Leaf Disease

As far as the agricultural domain is concerned, one of the most hot research areas of analysis is accurate prediction of leaf disease from the leaf images of a plant. The prediction of agricultural plant diseases bymeans of the image processing techniques will hence reduce the dependence on the farmers to safeguard their agricultural land and also their products. However, with the presence of noise, the leaf disease prediction is said to be hindered. To address this issue, in this paper, Covariance Kalman Geometric Graph-basedBernoulliClassifier (CKGG-BC) for Plant leaf disease prediction is proposed. The CKGG-BC method is split into three parts. To start with the plant leaf image provided as input, the Covariance Kalman Filtered Preprocessing modelintroduced for the image enhancement. Second, Geometric Graph-based Segmented Co-occurrence Feature Extraction model is applied to the preprocessed image to accurately segment the infected leaf areas and followed by which extracting the accurate infected leaf areas. Finally, Bernoulli Online Multiple Kernel Learning Classifier is applied for accurate plant leaf disease prediction with minimum classification error. The proposed method provides a significant refinement with respect to state-of-the-art methods. Even under complex background conditions, i.e., in the presence of noise, the averageaccuracy of the proposed method is said to be improved and hence paves mechanism for prediction of plant leaf disease in a significant manner. Experimentalresults exhibit the effectiveness of the proposed method in terms of computational overhead, accuracy, true positive rate and classification error respectively.

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 Smart Farming: IoT Based Plant Leaf Disease Detection and Prediction using Deep Neural Network with Image Processing

2019 ◽  
Vol 8 (9) ◽  
pp. 3081-3083 ◽  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Neural Network ◽  
Human Life ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Agricultural Laborers ◽  
Smart Farming ◽  
Texture Size

Agriculture plays a major role in human life. Almost 60% of the population is involved directly or indirectly in some agriculture activity. But Nowadays, farmers have quit agriculture and shifted to other sectors due to less adoption of automation and other reasons like increase in the requirement of agricultural laborers. So, Farmers now largely depend on adoption of cognitive solutions with technological advancements to acquire the benefits. Image processing and Internet of Things jointly produces new dimensions in the field of smart precision farming. This proposed methodology aims to create an approach for plant leaf disease detection based on deep neural network. This approach combines IoT and image processing which runs preprocessing and feature extraction techniques by considering different features such as color, texture, size and performs classification using deep learning model that expands to help identification of plant leaf disease

Sign in / Sign up

Export Citation Format

Share Document