Supervised Isomap for Plant Leaf Image Classification

2009 ◽  
pp. 627-634 ◽  
Author(s):  
Minggang Du ◽  
Shanwen Zhang ◽  
Hong Wang
Keyword(s):  
Image Classification ◽  
Plant Leaf ◽  
Leaf Image

scholarly journals Automated Quality Assessment of Crops Using CNN - Keras

2021 ◽  
pp. 641-645
Author(s):  
Meghashree ◽  
Alwyn Edison Mendonca ◽  
Ashika S Shetty
Keyword(s):  
Image Classification ◽  
Agricultural Production ◽  
Detection System ◽  
Cost Effective ◽  
Farming System ◽  
Modern Method ◽  
Innovative Technology ◽  
Disease Detection ◽  
Agricultural Field ◽  
Plant Leaf

Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.

An Improved Leaf Area Measurement Based on Image Processing

2012 ◽  
Vol 182-183 ◽  
pp. 624-628
Author(s):  
Dian Yuan Han
Keyword(s):  
Image Processing ◽  
Leaf Area ◽  
Good Accuracy ◽  
Area Measurement ◽  
Scanning Method ◽  
Plant Leaf ◽  
Unknown Area ◽  
Leaf Image ◽  
Background Area

This paper concerns the plant leaf area measurement based on improved image processing. Firstly, the referenced rectangle was detected with 2-side scanning method. Then the leaf region was segmented according to 2G-R-B of every pixel with two different thresholds, and by using of dilatation operation, the trimap of leaf image was got. Next the pixels in unknown area were classified to the foreground or background area with improved knockout method and the exact leaf was segmented. Lastly, the leaf area was calculated according to the pixels proportion between leaf region and the referenced rectangle. Experiment results show this method has good accuracy and rapid speed.

scholarly journals Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021 ◽  
Vol 8 ◽  
Author(s):  
Sufola Das Chagas Silva Araujo ◽  
V. S. Malemath ◽  
K. Meenakshi Sundaram
Keyword(s):  
Feature Learning ◽  
Region Of Interest ◽  
Physical Method ◽  
Image Database ◽  
Features Extraction ◽  
Plant Leaves ◽  
Rotation Invariant ◽  
Plant Leaf ◽  
Plant Image ◽  
Leaf Image

Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

scholarly journals A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering

2012 ◽  
Vol 44 (13) ◽  
pp. 10-20 ◽  
Author(s):  
N. Valliammal ◽  
S.N.Geethalakshmi S.N.Geethalakshmi
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Plant Leaf ◽  
Novel Approach ◽  
Leaf Image

scholarly journals Venation Skeleton-Based Modeling Plant Leaf Wilting

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Shenglian Lu ◽  
Chunjiang Zhao ◽  
Xinyu Guo
Keyword(s):  
Surface Deformation ◽  
Leaf Surface ◽  
Three Dimensional ◽  
Plant Leaf ◽  
Biological Responses ◽  
Leaf Image

A venation skeleton-driven method for modeling and animating plant leaf wilting is presented. The proposed method includes five principal processes. Firstly, a three-dimensional leaf skeleton is constructed from a leaf image, and the leaf skeleton is further used to generate a detailed mesh for the leaf surface. Then a venation skeleton is generated interactively from the leaf skeleton. Each vein in the venation skeleton consists of a segmented vertices string. Thirdly, each vertex in the leaf mesh is banded to the nearest vertex in the venation skeleton. We then deform the venation skeleton by controlling the movement of each vertex in the venation skeleton by rotating it around a fixed vector. Finally, the leaf mesh is mapped to the deformed venation skeleton, as such the deformation of the mesh follows the deformation of the venation skeleton. The proposed techniques have been applied to simulate plant leaf surface deformation resulted from biological responses of plant wilting.

Fine-Grained Plant Leaf Image Retrieval Using Local Angle Co-occurrence Histograms

2021 ◽  
Author(s):  
Xin Chen ◽  
Jiawei You ◽  
Hui Tang ◽  
Bin Wang ◽  
Yongsheng Gao
Keyword(s):  
Image Retrieval ◽  
Plant Leaf ◽  
Fine Grained ◽  
Local Angle ◽  
Leaf Image
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