Automatic Paddy Leaf Disease Detection Based on GLCM Using Multiclass Support Vector Machine

The paddy leaf diseases have increased rapidly in the recent years because of globalization, environmental pollution and climate changes which reduce the production of rice and economy of the country. For healthy growth of rice plants there is a need of automatic system which can detect the paddy diseases automatically on time to give the proper treatment for the affected plants. In this paper, we proposed a methodology to develop an automatic system for detect the paddy disease which are Paddy Blast Disease, Brown Spot Disease, Narrow Brown Spot Disease using MATLAB. This paper concentrate on the image processing techniques used to enhance the quality of the image and Multiclass Support Vector Machine to classify the paddy diseases. The methodology involves image acquisition, pre-processing, segmentation, feature extraction and classification of the paddy diseases. Image segmentation technique is used to detect infected parts of leaf by using canny edge detection, multilevel thresholding and region growing techniques. We extract texture features using GLCM (grey level co- occurrence matrix) techniques, additionally we extract color and shape features to improve the accuracy of the framework   and use Multiclass Support Vector Machine for classification. We achieved 87.5% accuracy for the test dataset.&nbsp

Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation

There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease

A Novel Plant Leaf Ailment Recognition Method using Image Processing Algorithms

In the 21st Century, agriculture still remains the major source of food for human beings and it has far shadowed other sources such as hunting, fishing and gathering. Since environmental conditions are beyond the scope of human control, plant illness identification is acting as a critical position in the agricultural field. This paper suggests a method to replace the traditional methods of identifying disease through the use of “image-processing” techniques. In this study, an image of the leaf of a diseased plant has been taken using a digital camera. Three segmentation algorithms namely Green Pixel Masking, “CIE L*a*b colour space” extraction and H element of HSV extraction have been used to split the image into diseased and healthy regions. The diseased region is then used to calculate 13 parameters which are utilized as inputs by a pre-trained neural network which utilizes “feed-forward back propagation algorithm” to determine the final output. The proposed methodology has achieved a maximum accuracy of 95.62% for Apple leaves, 91.62% for Grape leaves and 91.1% for Tomato leaves

A Novel Plant Leaf Ailment Recognition Method using Image Processing Algorithms

979-984In the 21st Century, agriculture still remains the major source of food for human beings and it has far shadowed other sources such as hunting, fishing and gathering. Since environmental conditions are beyond the scope of human control, plant illness identification is acting as a critical position in the agricultural field. This paper suggests a method to replace the traditional methods of identifying disease through the use of “image-processing” techniques. In this study, an image of the leaf of a diseased plant has been taken using a digital camera. Three segmentation algorithms namely Green Pixel Masking, “CIE L*a*b colour space” extraction and H element of HSV extraction have been used to split the image into diseased and healthy regions. The diseased region is then used to calculate 13 parameters which are utilized as inputs by a pre-trained neural network which utilizes “feed-forward back propagation algorithm” to determine the final output. The proposed methodology has achieved a maximum accuracy of 95.62% for Apple leaves, 91.62% for Grape leaves and 91.1% for Tomato leaves

Parametric evaluation of segmentation techniques for paddy diseases analysis

In most paddy plant diseases, the leaf is the primary source of information for image-based disease identification and classification. Image segmentation is an important step in the plant disease analysis process. It is used to separate the normal part of the leaf from the disease-affected part of the leaf. In this paper diseases like Bacterial leaf blight, Brown spot, and Leaf smut are segmented using existing, K-means clustering, the Otsu thresholding method. Color space-based segmentation is newly proposed for paddy disease analysis. The intelligence of segmentation techniques is evaluated using the Error Rate and Overlap Rate across the three paddy diseases namely, Bacterial Leaf Blight (BLB), Brown Spot (BS) and Leaf Smut (LS). The results were compared among the Otsu, K-means and color thresholding segmentation techniques. The results revealed that, the color thresholding method using the Lab model emerged as a novel segmentation method for all three paddy diseases with an average Error Rate (ER) and Overlap Rate (OR) of [0.36, 0.95]. The proposed work is carried out in the department of Electronics and Communication research centre at Ballari Institute of Technology and Management, Ballari, Karnataka during the period from August 2022 to February 2023 with the expert suggestions of the plant pathologist, from the University of Agricultural Science, Dharwad, Karnataka

The Development of Diseases Identification System in Paddy Plant Using Image Processing Technique

There are three types of paddy leaf disease that have similar symptoms, making it difficult for farmers to identify them, namely Blast Disease, Brown-Spot Disease, and Narrow Brown-Spot Disease. This paper aims to develop an application to identify paddy leaf disease automatically. Several important aspects of the development of software engineering such as usability, interactivity, and simplicity have been considered. Image processing techniques, namely Blobs analysis and color segmentation are used to get the characteristics of diseased leaf; these characteristics are then used to identify the type of diseases using a rule-based expert system. The results obtained indicate that the developed system recognition capability is considered satisfactory with an accuracy of 94.7%

Identification of paddy leaf diseases based on texture analysis of Blobs and color segmentation

There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease

Classification Models for Plant Diseases Diagnosis: A Review

Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved

UAV Remote Sensing: An Innovative Tool for Detection and Management of Rice Diseases

Unmanned aerial vehicle (UAV) remote sensing is a new alternative to traditional diagnosis and detection of rice diseases by visual symptoms, providing quick, accurate and large coverage disease detection. UAV remote sensing offers an unprecedented spectral, spatial, and temporal resolution that can distinguish diseased plant tissue from healthy tissue based on the characteristics of disease symptoms. Research has been conducted on using RGB sensor, multispectral sensor, and hyperspectral sensor for successful detection and quantification of sheath blight (Rhizoctonia solani), using multispectral sensor to accurately detect narrow brown leaf spot (Cercospora janseana), and using infrared thermal sensor for detecting the occurrence of rice blast (Magnaporthe oryzae). UAV can also be used for aerial application, and UAV spraying has become a new means for control of rice sheath blight and other crop diseases in many countries, especially China and Japan. UAV spraying can operate at low altitudes and various speeds, making it suitable for situations where arial and ground applications are unavailable or infeasible and where precision applications are needed. Along with advances in digitalization and artificial intelligence for precision application across fertilizer, pest and crop management needs, this UAV technology will become a core tool in a farmer’s precision equipment mix in the future

Disease Management and Microalgal Biofertilization for Rice Production

Sheath blight (ShB) caused by Rhizoctonia solani AG1-IA, narrow brown leaf spot (NBLS) caused by Cercospora janseana and fertility are among the most important factors limiting rice production in the U.S. Greenhouse experiments were conducted to better understand the effects of temperature and cultivar resistance on the biocontrol efficacy of Bacillus subtilis strain MBI 600 for management of ShB. Also, lab and field trials were conducted to evaluate the efficacy of fungicides for control of C. janseana and NBLS. In addition, greenhouse and field trials were conducted to explore the use of microalgae-based biofertilizers for rice production. In the first study in the greenhouse, plants of two rice cultivars (moderately resistant and susceptible to ShB) were spray treated with strain MBI 600 and subjected to different temperatures for 24 hours in dew growth chambers. Disease severity was assessed after 8 days of incubation. In the second study under in vitro conditions, fungicide sensitivity of C. janseana based on the percentage relative germination and the effective concentration that inhibited 50% of conidia germination were assessed. A field trial was also conducted in 2012 and 2014 to evaluate the efficacy of fungicides for control of NBLS and yield improvement. In the third study, greenhouse and field trials were conducted to evaluate the effects of N2-fixing cyanobacterial biofertilizer, microalgal biomass concentrate, and urea fertilizer on rice plant height and yield. In the first study, temperature significantly affected the relative biocontrol efficacy of strain MBI 600 in reducing ShB development in either cultivar. Its efficacy linearly increased with the increase of temperature, reaching the maximum at 35 or 40°C. In the second study, the succinate dehydrogenase inhibitor fungicides fluxapyroxad and flutolanil were most and least effective, respectively, in inhibiting C. janseana conidia, indicating that there was no cross-resistance between fluxapyroxad and flutolanil. Fluxapyroxad, propiconazole alone and in combination with azoxystrobin or trifloxystrobin were highly effective controlling NBLS. However, azoxystrobin was not effective to control NBLS. In the third study in the greenhouse, microalgal biomass concentrate treatment significantly improved rice plant height. However, no biofertilizer treatments improved rice yield in the field