AU2021106139A4 - A system and method for detecting diseases in tomato plant - Google Patents
A system and method for detecting diseases in tomato plant Download PDFInfo
- Publication number
- AU2021106139A4 AU2021106139A4 AU2021106139A AU2021106139A AU2021106139A4 AU 2021106139 A4 AU2021106139 A4 AU 2021106139A4 AU 2021106139 A AU2021106139 A AU 2021106139A AU 2021106139 A AU2021106139 A AU 2021106139A AU 2021106139 A4 AU2021106139 A4 AU 2021106139A4
- Authority
- AU
- Australia
- Prior art keywords
- disease
- images
- diseases
- leaf
- detecting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Ceased
Links
- 201000010099 disease Diseases 0.000 title claims abstract description 114
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 114
- 238000000034 method Methods 0.000 title claims abstract description 30
- 240000003768 Solanum lycopersicum Species 0.000 title description 29
- 235000007688 Lycopersicon esculentum Nutrition 0.000 claims abstract description 16
- 241000196324 Embryophyta Species 0.000 claims abstract description 10
- 238000011161 development Methods 0.000 claims abstract description 8
- 241000227653 Lycopersicon Species 0.000 claims abstract 8
- 238000012545 processing Methods 0.000 claims description 11
- 230000002708 enhancing effect Effects 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 abstract description 4
- 229920003266 Leaf® Polymers 0.000 description 27
- 230000008901 benefit Effects 0.000 description 9
- 230000008569 process Effects 0.000 description 6
- 239000003795 chemical substances by application Substances 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 208000024891 symptom Diseases 0.000 description 3
- 238000003745 diagnosis Methods 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 241001157813 Cercospora Species 0.000 description 1
- 241000233866 Fungi Species 0.000 description 1
- 241000238631 Hexapoda Species 0.000 description 1
- 241000700605 Viruses Species 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000003784 poor nutrition Nutrition 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0098—Plants or trees
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/483—Physical analysis of biological material
- G01N33/4833—Physical analysis of biological material of solid biological material, e.g. tissue samples, cell cultures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
- A01B79/00—Methods for working soil
- A01B79/005—Precision agriculture
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G22/00—Cultivation of specific crops or plants not otherwise provided for
- A01G22/05—Fruit crops, e.g. strawberries, tomatoes or cucumbers
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Business, Economics & Management (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Molecular Biology (AREA)
- Mining & Mineral Resources (AREA)
- Hematology (AREA)
- Biophysics (AREA)
- Botany (AREA)
- Wood Science & Technology (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Urology & Nephrology (AREA)
- Optics & Photonics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The present disclosure relates to a system and method for detecting diseases in tomato
plant. The present disclosure develops a computer vision system for diagnosing the diseases in
tomato plant using leaf images and other parts of the plant, wherein the diseases are diagnosed at
various stages of development. The proposed system also measures the severity of the disease by
quantifying the disease. The method for diagnosing disease comprises: detection of tomato leaf
disease, wherein the images are processed and then features are extracted from them; recognizing
the type of disease by classifying them; quantifying the disease by measuring the severity of it.
The present invention also deals with the problems of complex background of the leaf images,
various capturing conditions and multiple disease of the leaf. The datasets for diagnosing the
disease are collected from various sources on internet such as Plant Village Disease
Classification Challenge.
10
Description
The present disclosure relates to a system and method for detecting diseases in tomato plant.
In agriculture the tomato plants are prone to many diseases whose symptoms are visible from different parts of the plants such as leaves. Due to these diseases tons of crops are lost each year which in turn affects the economical conditions of the farmers. The manual diagnosis of the disease is a very time consuming and tedious process, therefore for recognizing the disease effectively and efficiently, image processing techniques are used for recognizing and classifying the tomato leaf disease quickly. The major factors which affect the tomatoes are living agents and non living agents, wherein the living agents are such as insects, bacteria, fungi, and viruses, and wherein the non living agents are temperature, low sunlight, and poor nutrition in the soil.
Diagnosing the disease in tomato plants at the very initial stage is very essential which can be done using an automatic disease detection technique which is a computational method especially for tomato leafs, wherein the classification and detection of disease is done using leaf image automatic. Therefore there is a need for a system and method for detecting diseases in tomato plant.
The present disclosure relates to a system and method for detecting diseases in tomato plant. The main objective of the disclosure is to develop an computer vision system that can accurately diagnose a disease at any stage in a tomato plants on the basis of leaf images with complex backgrounds which are provided as input data to train the algorithm, wherein the images are captured in different capturing conditions. In this disclosure the diseases are diagnosed in three different steps which include: detection of disease in tomato leaf; classification of the disease which means recognizing the type of disease; quantifying the disease which means measuring the severity the disease. The proposed algorithm can deal with the problems such as complex background of the leaf images, different capturing conditions, and leaf having multiple diseases. The synthesized dataset for the algorithm is collected from the PlantVillage dataset and many other datasets which are available online. The method for diagnosing a disease comprises steps such as: image processing which means removing noise from the images; image enhancement which means enhancing the images for extracting features from the image; feature extracting means extracting features from the images for detecting the diseases; and classification which means classifying the diseases with the help of extracted features.
The present disclosure also seeks to provide a system for detecting disease in tomato plant. The system comprises: a processing unit for preparing the leaf images for the diagnosing the disease, wherein the extra background information and noise is removed from the images and images are enhanced for increasing the accuracy for detecting diseases; a feature extraction unit for extracting features from the pre-processed images for disease diagnosing by considering various factors such as leaf images having complex background and different capturing conditions; and a classification unit for classifying the diseases according to the stage of development and types of diseases, wherein the severity of the disease is also measured by quantifying the disease.
The present disclosure seeks to provide a method for detecting diseases in tomato plant. The method comprises: processing the images collected from the datasets for noise and extra background removal and enhancing the images for improving the accuracy in detecting the disease; extracting features from the processed images for detecting of tomato leaf disease, wherein more than one disease can be detected at different stages and capturing conditions; classifying the diseases according to the types and stage of disease; and quantifying the diseases by measuring the severity of the diseases.
An objective of the present disclosure is to provide a system and method for detecting diseases in tomato plant.
Another object of the present disclosure is to obtain synthesized datasets from different datasets which are available online.
Another object of the present disclosure is to process the data images for noise and background removing and enhancing the images before extracting the features.
Another object of the present disclosure is to develop a computer vision system for diagnosing the disease in tomato plants, measuring the severity of disease.
Yet, another object of the present disclosure is to deal with the tomato leaf images which have more than one disease and the boundary of different diseases which have same features.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a block diagram of a system for detecting disease in tomato plant in accordance with an embodiment of the present disclosure;
Figure 2 illustrates a flow chart of a method for detecting diseases in tomato plant in accordance with an embodiment of the present disclosure;
Figure 3 illustrates a sample infected leaf in accordance with an embodiment of the present disclosure;
Figure 4 illustrates the classes of crop disease pairs in Plant Village dataset in accordance with an embodiment of the present disclosure;
Figure 5illustrates the different tomato leaf diseases at various stages of development.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
Figure 1 illustrates a block diagram of a system for detecting disease in tomato plant in accordance with an embodiment of the present disclosure. The system 100 includes, a processing unit 102 for preparing the leaf images for the diagnosing the disease, wherein the extra background information and noise is removed from the images and images are enhanced for increasing the accuracy for detecting diseases;
In an embodiment, a feature extraction unit 104 is used for extracting features from the pre-processed images for disease diagnosing by considering various factors such as leaf images having complex background and different capturing conditions.
In an embodiment, a classification unit 106 is used for classifying the diseases according to the stage of development and types of diseases, wherein the severity of the disease is also measured by quantifying the disease.
In an embodiment, the system can detect more than one disease in tomato leaf even at different stages of disease and different capturing conditions and also measures the severity of the disease.
In an embodiment, the datasets are collected from various online sources such as PlantVillage dataset along with manual collection from fields of agriculture.
In an embodiment, the images collected from the datasets are turned into grayscale version and finally the extra background is removed from the images.
Figure 2 illustrates a flow chart of a method for detecting diseases in tomato plant in accordance with an embodiment of the present disclosure. At step 202 the method 200 includes, processing the images collected from the datasets for noise and extra background removal and enhancing the images for improving the accuracy in detecting the disease.
At step 204 the method 200 includes, extracting features from the processed images for detecting of tomato leaf disease, wherein more than one disease can be detected at different stages and capturing conditions.
At step 206 the method 200 includes, classifying the diseases according to the types and stage of disease.
At step 208 the method 200 includes, quantifying the diseases by measuring the severity of the diseases.
Figure 3 illustrates the challenges which are identified in the leaf images while diagnosing the disease in tomato plant. The figure (a) shows that leaf having complex background make it difficult to diagnose a disease and are identified as a challenge. The figure (b) shows the sample of leaf having capturing conditions, wherein the leaf is covered with shadow and hence making it difficult to diagnose the disease. The figure (c) shows the variation in the symptoms of same disease. The figure (d) shows a coffee leaf which contains symptoms of rust and cercospora leaf spot.
Figure 4 illustrates the classes of crop disease pairs in Plant Village dataset in accordance with an embodiment of the present disclosure. The image processing starts with the Plant Village dataset as it is, and then the image is turned into grayscale version of the Plant Village dataset and finally the extra background information are removed from the images for extracting the features, wherein the extra background might introduce some inherent bias in the dataset due to the regularized process of data collection from Plant Village dataset.
Figure 5 focuses on the analysis of tomato leaf disease diagnosis at various stages of development including high, medium, low and healthy leaf. Nine tomato diseases from the Plant Village dataset are considered for experimentation. The images are categorized into different stages of development by visual inspection for preparation of the dataset. During this work tomato leaf diseases differentiated at various stages of development using color and texture features. Normal leaf images can also be differentiated from diseased images in the similar way.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
Claims (5)
1. A system for detecting diseases in tomato plant, the system comprises:
a processing unit for preparing the leaf images for the diagnosing the disease, wherein the extra background information and noise is removed from the images and images are enhanced for increasing the accuracy for detecting diseases; a feature extraction unit for extracting features from the pre-processed images for disease diagnosing by considering various factors such as leaf images having complex background and different capturing conditions; and a classification unit for classifying the diseases according to the stage of development and types of diseases, wherein the severity of the disease is also measured by quantifying the disease.
2. The system as claimed in claim 1, wherein the system can detect more than one disease in tomato leaf even at different stages of disease and different capturing conditions and also measures the severity of the disease.
3. The system as claimed in claim 1, wherein the datasets are collected from various online sources such as Plant Village dataset, along with manual collection from fields of agriculture.
4. The system as claimed in claim 1, wherein the images collected from the datasets are turned into grayscale version and finally the extra background is removed from the images.
5. A method for detecting disease in tomato plant, the method comprises:
processing the images collected from the datasets for noise and extra background removal and enhancing the images for improving the accuracy in detecting the disease; extracting features from the processed images for detecting of tomato leaf disease, wherein more than one disease can be detected at different stages and capturing conditions; classifying the diseases according to the types and stage of disease; and quantifying the diseases by measuring the severity of the diseases.
Processing unit 102 Feature extraction unit 104
Classification unit 106
Figure 1 processing the images collected from the datasets for noise and extra background removal and enhancing the images for improving the accuracy in 202 detecting the disease extracting features from the processed images for detecting of tomato leaf 204 disease, wherein more than one disease can be detected at different stages and capturing conditions; classifying the diseases according to the types and stage of disease 206
208 quantifying the diseases by measuring the severity of the diseases.
Figure 2
(a) (b)
(c) (d)
Figure 3
Figure 4
Figure 5
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021106139A AU2021106139A4 (en) | 2021-08-20 | 2021-08-20 | A system and method for detecting diseases in tomato plant |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021106139A AU2021106139A4 (en) | 2021-08-20 | 2021-08-20 | A system and method for detecting diseases in tomato plant |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021106139A4 true AU2021106139A4 (en) | 2021-12-02 |
Family
ID=78716507
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021106139A Ceased AU2021106139A4 (en) | 2021-08-20 | 2021-08-20 | A system and method for detecting diseases in tomato plant |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021106139A4 (en) |
-
2021
- 2021-08-20 AU AU2021106139A patent/AU2021106139A4/en not_active Ceased
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Esgario et al. | Deep learning for classification and severity estimation of coffee leaf biotic stress | |
| CN108764286B (en) | Classification and identification method of feature points in blood vessel image based on transfer learning | |
| Mukherjee et al. | Damaged paddy leaf detection using image processing | |
| CN110969204B (en) | Sample classification system based on fusion of magnetic resonance image and digital pathology image | |
| Pavithra et al. | Paddy leaf disease detection using SVM classifier | |
| Ngugi et al. | A new approach to learning and recognizing leaf diseases from individual lesions using convolutional neural networks | |
| CN112257711B (en) | Method for detecting damage fault of railway wagon floor | |
| CN111007013B (en) | Crop rotation fallow remote sensing monitoring method and device for northeast cold region | |
| Pujari et al. | Statistical methods for quantitatively detecting fungal disease from fruits’ images | |
| CN103793709A (en) | Cell recognition method and device, and urine analyzer | |
| Patki et al. | Cotton leaf disease detection & classification using multi SVM | |
| CN115439654B (en) | Method and system for finely dividing weakly supervised farmland plots under dynamic constraint | |
| Bondre et al. | Review on leaf diseases detection using deep learning | |
| Helmholz et al. | Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images | |
| Du et al. | Retinal microaneurysm detection based on transformation splicing and multi-context ensemble learning | |
| Ihsanuddin et al. | Identification of Bacterial Leaf Blight and Brown Spot Disease In Rice Plants With Image Processing Approach | |
| Jobin et al. | Plant identification based on fractal refinement technique (FRT) | |
| Mohtashamian et al. | Automated plant species identification using leaf shape-based classification techniques: a case study on Iranian Maples | |
| AU2021106139A4 (en) | A system and method for detecting diseases in tomato plant | |
| Tejonidhi et al. | Plant disease analysis using histogram matching based on Bhattacharya's distance calculation | |
| CN116310913B (en) | Natural resource investigation monitoring method and device based on unmanned aerial vehicle measurement technology | |
| Dhole et al. | Review of leaf unhealthy region detection using image processing techniques | |
| Lili et al. | Classification of herbs plant diseases via hierarchical dynamic artificial neural network after image removal using kernel regression framework | |
| CN115409810A (en) | Sample selection method, device and system for remote sensing image | |
| Jelinek et al. | Quality control and multi-lesion detection in automated retinopathy classification using a visual words dictionary |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |