Paddy Leaf Disease Research Articles

Detection and Recognition of Paddy Leaf Diseases Using Image Processing

Detection and Recognition of Paddy Leaf Diseases Using Image Processing

OCL Based Approach for Sustainable ML Model Development

It became a bottleneck for the Machine Learning (ML) researchers to select/develop a sustainable model for a particular problem. Hence, there is a need for an approach to prepare a model with all constraints of the software system. The proposed approach is based on Object Constraint Language (OCL) which is a declarative language for writing constraints on software artifacts, it is widely used for effective representation of Functional Requirements (FR’s) and Non-Functional Requirements (NFR’s). In the proposed system, the paddy leaf disease identification system is considered and proposed Leaf Identification Constraints (LIC) and Leaf Disease Identification Constraints (LDIC) based on OCL, for the proposed constraints the Convolutional Neural Network (CNN) is chosen, as it can handle diverse range of input data and large volume of concurrent requests. To satisfy other constraints of the system, the Auto encoders are used along with CNN and the input data was take in the form of thermal imaging. This system was evaluated with test data and validation data and obtained the accuracy of 90.6%. And 84.8 was attained by earlier researchers before this approach.

Enhancing the Paddy Disease Classification by Using Cross‐Validation Strategy for Artificial Neural Network over Baseline Classifiers

Pathogens, including viruses, bacteria, and fungus, are the biotic agents that cause illnesses in crops and are the major cause of yield losses of up to 16 percent in certain parts of the globe. Pathogens are the primary cause of yield losses in some parts of the world. Deep learning algorithms, which are at the cutting edge of technology, are now being used to identify crop disease at an earlier stage. Supervised learning (support vector machine and K‐nearest neighbor), ensemble learning (random forest and AdaBoost), and deep learning approaches were used in this study to suggest a classification of paddy leaf diseases, including bacteria leaf blight, blast, hispa, leaf spot, and leaf folder (neural networks). In order to evaluate the performance of the learning approaches, accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the interpretation (ROC and AUC). According to the results of the investigation, when the fold value grows, the value of the evaluation metrics (AUC, CA, F1, precision, and recall) increases in a progressive manner, i.e., the 0.001 value increases as compared to the values obtained with the previous folds. When comparing the neural network to the baseline classifiers, the assessment metrics demonstrate that the neural network performs much better.

Radial basis function neural network and salp swarm algorithm for paddy leaf diseases classification in Thanjavur, Tamilnadu geographical region

Radial basis function neural network and salp swarm algorithm for paddy leaf diseases classification in Thanjavur, Tamilnadu geographical region

Deep Learning based Paddy Leaf Disease Identification

Paddy is one of the most important agricultural commodities in the world with a number of cuisines scattered across the globe, which are incomplete without it. In developing countries like India, paddy has spurred agriculture driven growth in the past century, when export of agricultural produce was the major source of foreign exchange. At times, the prices face a blow from the demand side, while at times facing drastic conditions on the supply side. In such years, farmers often cannot afford the services of agricultural consultants for tasks such as detection of leaf diseases and addressing them at the earliest. The solution we have proposed, is a low cost system that uses CNN Algorithm to detect leaf diseases in the leaves of paddy plants with an average 93.27% accuracy to make things easier for both the farmers as well as consumers, since this would balance the prices at a median price, which is both profit-earning for farmers as well as affordable to the consumers at all times of the year.

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

AbstractPaddy is the most significant crop utilized by more than 2.6 billion people. The paddy crops are affected by various diseases that are unidentified and reduced the production of crop yield. Nowadays, the plants diseases and pests spread increasingly due to the climate change, trade, and globalization. The plant pathogens can be viral, fungal, nematodes or bacterial that affects all parts of the plants. The challenging tasks are to determine the symptoms and identify the controlling measures of the plant diseases. The plant leaves can be affected by numerous diseases, which results in destruction in terms of crop field to various social and economic aspects. The deep structured architectures and machine learning are implemented in the conventional models for detecting the leaf diseases. Hence, the main intention of this study is to develop the novel model for paddy leaf disease recognition using the hybrid deep learning. Initially, the input paddy leaf images are collected from standard sources that undergo filtering and contrast enhancement approaches. Further, the segmentation of the abnormal region of the paddy leaf is done by “adaptive K‐means clustering.” This is also accomplished by the Fitness Sorted‐Shark Smell Optimization (FS‐SSO). With the segmented images, the recognition of the disease is performed by the hybrid deep learning using the Resnet and YOLO classifier. As the modification, the fully connected layer of the ResNet model is replaced by the YOLO classifier for disease recognition. The significant parameters of the hybrid deep learning are optimized by the FS‐SSO for attaining the high recognition rate. Experimental analysis is performed for computing the performance metrics and the accuracy of the classification for evaluating the efficiency of the suggested method.

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology. Nevertheless, the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge. This approach is time-consuming, and high computational resources are mandatory. In this research, we propose a novel method based on Mutant Particle swarm optimization (MUT-PSO) Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification. Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network (MUTPSO-CNN) can find optimum CNN architecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy, precision/recall, and execution time.

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this disease can avoid huge loss incurred from less crop productivity. Some of the recently-developed computer vision and Deep Learning (DL) approaches can be commonly employed in designing effective models for rice plant disease detection and classification processes. With this motivation, the current research work devises an Efficient Deep Learning based Fusion Model for Rice Plant Disease (EDLFM-RPD) detection and classification. The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner. In addition, EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions. The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix (GLCM) and Inception-based deep features to derive the features. Finally, Salp Swarm Optimization with Fuzzy Support Vector Machine (FSVM) model is utilized for classification. In order to validate the enhanced outcomes of EDLFM-RPD technique, a series of simulations was conducted. The results were assessed under different measures. The obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

Identification of Paddy Leaf Diseases using Evolutionary and Machine Learning Methods

In the field of agriculture, especially paddy plants, there is a demand for research to classify the paddy diseases at early stages. This is feasible if there are automated systems that can assist the farmers to recognize the paddy diseases from the paddy leaf images of the plants. The recognition of agricultural plant diseases by utilizing the image-processing and machine learning techniques can certainly minimize the reliance on the farmers to protect the yield of paddy crops. In this paper, an attempt has been made to pre-process the images to prepare the feature-set for Classifiers and then feature extraction algorithms are used to extract the relevant features from the processed images. The feature-set is then supplied to the classifiers for identification of Paddy Leaf diseases. The usage of cascaded classifiers has been explored to detect the diseases of paddy leaves. An attempt has also been made to use genetic algorithm with nearest neighbour algorithm to identify the diseases of paddy leaves. The proposed automated system can be used on Android , Windows platform and Apple platform for quickly identifying the paddy leaf diseases as the entire implementation has been performed using MATLAB. The proposed automated system can certainly help the farmers to classify the diseased paddy leaves at early stage to protect the crops from further damage.

Smart paddy field monitoring system using deep learning and IoT

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection

Bangladesh and India are significant paddy-cultivation countries in the globe. Paddy is the key producing crop in Bangladesh. In the last 11 years, the part of agriculture in Bangladesh's Gross Domestic Product (GDP) was contributing about 15.08 percent. But unfortunately, the farmers who are working so hard to grow this crop, have to face huge losses because of crop damages caused by various diseases of paddy. There are approximately more than 30 diseases of paddy leaf and among them, about 7-8 diseases are quite common in Bangladesh. Paddy leaf diseases like Brown Spot Disease, Blast Disease, Bacterial Leaf Blight, etc. are very well known and most affecting one among different paddy leaf diseases. These diseases are hampering the growth and productivity of paddy plants which can lead to great ecological and economical losses. If these diseases can be detected at an early stage with great accuracy and in a short time, then the damages to the crops can be greatly reduced and the losses of the farmers can be prevented. This paper has worked on 4 types of diseases and one healthy leaf class of the paddy. The main goal of this paper is to provide the best results for paddy leaf disease detection through an automated detection approach with the deep learning CNN models that can achieve the highest accuracy instead of the traditional lengthy manual disease detection process where the accuracy is also greatly questionable. It has analyzed four models such as VGG-19, Inception-Resnet-V2, ResNet-101, Xception, and achieved better accuracy from Inception-ResNet-V2 is 92.68%.

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.

Paddy Leaf Disease Analysis with Hybrid Algorithms Using Deep Learning Techniques

According to the standards of India the financial, political and social security depend legitimately just as in a roundabout way on the yearly creation of rice. Wellspring of pay of hundred a large number of individuals relies just upon rice creation and nothing else included according to the report of International Rice Research Institute (IRRI), 37% of the rice yield misfortune is because of illnesses. In a general public the rancher can deal with crop on-time with suitable treatment. By a yield shrewd rice is the hugest humanoid sustenance on the planet, which can be taken care of legitimately than some other gather. Sequentially, the fundamental target of cultivating is to yield and feed nourishment to the country. In this way, these leaf ailments in any structures in rice crop will in general reason decrease in quality, yield and financial movement separately. This venture reports a novel methodology for recognition and distinguishing proof of rice leaf infections by utilizing different propelled calculations, for example, VGG16, Alex net, and so on to expand the precision to more prominent level. To apply misfortune minimization procedure, for example, straight out cross entropy to build the precision. It expands precision with utilization of mixture calculations by performing tweaking. Just as proposes the kind of substance compost to be utilized when any sort of malady gets distinguished. Just as proposes the sort of compound compost to be utilized when any kind of sickness gets recognized.

Detection and Recognition of Paddy Plant Leaf Diseases using Machine Learning Technique

Rice is one of the important food crops and the most staple food for half of the world population. Farmers are often faces several obstacles in paddy production because of various paddy leaf diseases. As a result, rice production is extensively reduced. For finding the paddy plant leaf diseases, there are many techniques are available in the computer vision-based area. Now, it is the main concern to fast and accurate recognition of paddy plant diseases in the initial stage. For this reason, we proposed a better approach for early paddy plant leaf disease detection by using simple image processing and machine learning techniques. There are four types of paddy leaf diseases are highlighted in this paper; which are Brown Spot, Sheath Blight, Blast Disease and Narrow Brown Spot. To do this, at first the required normal and diseased paddy plant leaf images are captured directly from different paddy fields. The unnecessary background of the leaves images are eliminated by using mask in the pre-processing section. Then output is fed into the segmentation part where K-means clustering is used to separate the normal portion and diseased portion of the leaf images. Finally, the mentioned diseases are classified using Support Vector Machine (SVM) algorithm. The accuracy of the system is 94%. This technique can be also applied anywhere in the agriculture industry for plant leaf diseases detection.

Paddy leaf diseases recognition and classification using PCA and BFO-DNN algorithm by image processing

Currently, agriculture has become the most significant approach than it has been used before a few years back, when the plants are used for feeding the human and flora and fauna. Presently, the plants have been used to create the electricity and other type of the power to enhance the living situations of the social beings. Due to this, it is required for the suitable care of the plants to acquire the maximum advantage The main region that required main consideration is the cutting plant leaves diseases. A number of the diseases affect the leaves that may cause maximum destruction to different economic and social aspect. It may also cause high environmental losses. In the proposed system, study the different leaf infections using detection and classification technique in image processing. Initially, various paddy leaves acquire using digital pictures. After that, RGB model converted into the HSV model for resizing the picture using k mean clustering with image segmentation. Then, the specific features are extracted using the PCA algorithm. Moreover, the feature extraction and BFO-DNN method implemented for classification of the paddy leaf diseases. This classification method is used to improve the detection rate and reduce the entropy loss. It is highly efficient and accurate to detect or recognize the disease image with different number of categories (Bacteria light, sheath rot, Brown spot and Normal etc.). Experimental analysis is done to calculate the performance metric like as accuracy, TPR, TNR,FDR, Cross Entropy and FPR. Then, the comparative analysis of the existing parameters compared to the current parameters. The proposed system performance value of accuracy is 98% with hybrid BFOA-DNN, accuracy, value is 97 and DNN 93.50 percentage . The research system performance value of Cross Entropy Loss is 0.0011 with hybrid BFOA-DNN, Entropy Loss value is 0.0100 and DNN 0.01700 per cent.

Automatic classification of paddy leaf disease

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Detection and Classification of Paddy Crop Disease using Deep Learning Techniques

Agricultural production plays a vital role in Indian economy. The biggest menace for a farmer is the various diseases that infect the crop. Quality and high production of crops is involved with factors like efficient detection of diseases in the crop. The disease detection though Naked-eye observation of expert can be prohibitively expensive and requires meticulous and scrupulous analysis to detect the disease. The existing systems on disease detection is not efficient enough in terms on real time basis. This paper presents an effective method for identification of paddy leaf disease. The proposed approaches involves pre-processing of input image and the paddy plant disease type is recognized using Gray-Level Co-occurrence Matrix (GLCM) technique and classifiers namely Artificial Neural Networks is used for better accuracy of detection. This method will be very useful to farmers to detect paddy diseases beforehand and thus prevent over usage of pesticides which in turn affects the crop production.

Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm

In the agriculture field, one of the recent research topics is recognition and classification of diseases from the leaf images of a plant. The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural products. In this paper, Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm is proposed. For the image acquisition the images of rice plant leaves are directly captured from the farm field for normal, bacterial blight, brown spot, sheath rot and blast diseases. In pre-processing, for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non-diseased part. For the segmentation of diseased portion, normal portion and background a clustering method is used. Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm (DNN_JOA). In order to precise the stability of this approach a feedback loop is generated in the post processing step. The experimental results are evaluated and compared with ANN, DAE and DNN. The proposed method achieved high accuracy of 98.9% for the blast affected, 95.78% for the bacterial blight, 92% for the sheath rot, 94% for the brown spot and 90.57% for the normal leaf image.

Paddy Leaf Disease Identification and Classification System A Review

Paddy Leaf Disease Identification and Classification System A Review

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%.