Methods of Health Improving Using Leaf Image Processing

2021 ◽  
Author(s):  
Bogdan-Cristian Savin ◽  
Mihaela Hnatiuc
Keyword(s):  
Image Processing ◽  
Leaf Image

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 PLANT LEAF IMAGE RECOGNITION SYSTEM

2021 ◽  
Vol 1 (1) ◽  
pp. 35-44
Author(s):  
Gaurav Kulkarni ◽  
◽  
Chandrashekhar Kumbhar
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Image Recognition ◽  
Recognition System ◽  
Vital Role ◽  
Rare Plant ◽  
Plant Recognition ◽  
Identification Techniques ◽  
Leaf Image ◽  
End Results

Plants play a vital role in our day-to-day life. Hence, a good understanding of plants is needed to help in identifying new or rare plant species. Such identification will in turn improve the drug industry, balance the ecosystem as well as the agricultural productivity and sustainability. We often come across various plants with different variety of leaves and flowers every single day. We try to recognize it, but we fail. So we need some system which can tell us about the leaf/flower instantly. So, to solve such problems, we introduce a plant recognition system (PRS) which tells you the details about a leaf by just uploading the image of the leaf. For this system, we use image processing and some identification techniques which can recognize the leaf by its structure, colour, shape etc and fetch the details about it and provide the details of it to the user. This paper gives a understanding about the different methods used under image processing and various methods and algorithm used to identify that leaf in a short and simple way. Object recognition and detection are techniques with similar end results and implementation approaches. Therefore, it requires heavy pre-processing and implements various processes to obtain the end results.

scholarly journals Disease Detection and Remote Monitoring in Chilli Crop Using Image Processing

2021 ◽  
Vol 9 (8) ◽  
pp. 2988-2995
Author(s):  
M Keerthi
Keyword(s):  
Image Processing ◽  
Crop Yield ◽  
Remote Monitoring ◽  
Plant Production ◽  
Disease Detection ◽  
Leaf Characteristics ◽  
Chilli Plant ◽  
Early Stages ◽  
Leaf Image

Abstract: Observations today have verified that the average crop yield in India is declining due to illnesses that have affected fully grown plants. Chilli plant production is tough due to the plant's vulnerability to a variety of microorganisms, infectious illnesses, and pests. Infections in the chilli plant impact areas such as the leaves and stems. In the early stages of diagnosing chilli illnesses, leaf characteristics are examined. The leaf image is taken and analyzed to determine the health of the chilli plant. Pesticides are currently being tested on chilli plants on a regular basis without first determining the needs of each plant. This ensures that pesticides are only used when diseased plants are discovered. Keywords: Infections in the chilli plant, chilli illnesses, characteristics are examined, Pesticides are currently being tested on chilli plants.

scholarly journals Leaf Image Recognition Based on Bag of Features

2020 ◽  
Vol 10 (15) ◽  
pp. 5177 ◽  
Author(s):  
Yaonan Zhang ◽  
Jing Cui ◽  
Zhaobin Wang ◽  
Jianfang Kang ◽  
Yufang Min
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Human Life ◽  
Texture Features ◽  
Output Pulse ◽  
Support Vector ◽  
Linear Discriminant ◽  
Bag Of Features ◽  
Good Ability ◽  
Leaf Image

Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.

Desease Identification In Plant Leaf Image of Chili (Capsicum Annum (L)) Using Image Processing and Automated Colour Equalization (ACE) Algorithm

2018 ◽  
Vol 1 (2) ◽  
pp. 99
Author(s):  
Basiroh Basiroh
Keyword(s):  
Image Processing ◽  
Agricultural Sector ◽  
Texture Features ◽  
Support Vector ◽  
Color Feature ◽  
Colour Feature ◽  
Agricultural Yield ◽  
Accuracy Of Results ◽  
Ace Algorithm ◽  
Leaf Image

The world of agriculture becomes one of the vital objects and one of the promising business prospects. To obtain optimal agricultural yield, the process of plant care and the way of planting should be really - maximal, because the main key in seeking maximum results in terms of quality and quantity. Harvest failures are the least desirable to farmers and crop failures are the number one scariest specter for cultivating farmers. Today's informatics technology has been developed in an effort to support increased yields in the agricultural sector. This study measured the level of accuracy of results ekstraksi texture and colour feature. This research method using SVM classification ( Support Vector Machine ) seeks image processing through analyzing with Automated Color Equalization (ACE). With this method the accuracy of the extraction results a combination of 80% texture features, color feature extraction, and a combination of 80% color feature texture

Cotton Leaf Disease Detection by Feature Extraction

2016 ◽  
pp. 89-104
Author(s):  
Savita N. Ghaiwat ◽  
Parul Arora
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Early Stage ◽  
Cotton Leaf ◽  
Control Methods ◽  
The Neural Network ◽  
The World ◽  
Leaf Image

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

scholarly journals Plant Disease Prediction System using Image Processing

2020 ◽  
pp. 162-167
Author(s):  
B Uma Jagadeswari ◽  
D Harshitha ◽  
G Vineela ◽  
B Siri ◽  
Yaragani Sowmya
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Negative Impact ◽  
Agricultural Productivity ◽  
Significant Loss ◽  
Plant Diseases ◽  
Disease Prediction ◽  
Agricultural Field ◽  
Processing Techniques ◽  
Leaf Image

Agriculture productivity is the major issue which affects the Indian economy. Crop cultivation plays an essential role in the agricultural field. Presently, the loss of food is mainly due to infected crops, which reflexively reduces the production rate. The major cause for decrease in the quality and amount of agricultural productivity is due to the diseases in plants. The occurrence of diseases in plants may result in significant loss in both quality and quantity of agricultural productivity. This can produce the negative impact on the countries whose economies are primarily dependent on the agriculture. Farmers encounter great difficulties in detecting and controlling plant diseases. Hence the detection of plant diseases in the earlier stages is very important to avoid the loss in terms of quality, quantity and finance. This paper mainly focuses on the approach based on image processing techniques that help farmers for detecting the diseases of plants by uploading leaf image to the system.

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