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

A brief study on rice diseases recognition and image classification: fusion deep belief network and S-particle swarm optimization algorithm

In the regions of southern Andhra Pradesh, rice brown spot, rice blast, and rice sheath blight have emerged as the most prevalent diseases. The goal of this research is to increase the precision and effectiveness of disease diagnosis by proposing a framework for the automated recognition and classification of rice diseases. Therefore, this work proposes a hybrid approach with multiple stages. Initially, the region of interest (ROI) is extracted from the dataset and test images. Then, the multiple features are extracted, such as color-moment-based features, grey-level cooccurrence matrix (GLCM)-based texture, and shape features. Then, the S-particle swarm optimization (SPSO) model selects the best features from the extracted features. Moreover, the deep belief network (DBN) model trained by SPSO is based on optimal features, which classify the different types of rice diseases. The SPSO algorithm also optimized the losses generated in the DBN model. The suggested model achieves a hit rate of 94.85% and an accuracy of 97.48% with the 10-fold cross-validation approach. The traditional machine learning (ML) model is significantly less accurate than the area under the receiver operating characteristic curve (AUC), which has an accuracy of 97.48%

Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important studyarea is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plantillnesses. The process of choosing a classification method is always challenging because the quality of the results can differ depending on the input data. K-Nearest Neighbor Classifier (KNN), Probabilistic NeuralNetwork (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis,Artificial Neural Network (ANN), and Fuzzy Logic are a few examples of diverse classification algorithms.Classifications of plant leaf diseases have many uses in a variety of industries, including agriculture andbiological research. Presymptomatic diagnosis and crop health information can aid in the ability to managepathogens through proper management approaches. Convolutional neural networks (CNNs) are the mostwidely used DL models for computer vision issues since they have proven to be very effective in tasks likepicture categorization, object detection, image segmentation, etc. The experimental findings demonstrate theproposed model's superior performance to pre-trained models such as VGG16 and InceptionV3. The range ofcategorization accuracy is 76% to 100%, based on

REVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES

The most important cereal crop in the world is rice (Oryza sativa). Over half of the world's population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt "eye" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning

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

Computational Approaches Based On Image Processing for Automated Disease Identification On Chili Leaf Images: A Review

Chili, an important crop whose fruit is used as a spice, is significantly hampered by the existence of chili diseases. While these diseases pose a significant concern to farmers since they impair the supply of spices to the market, they can be managed and monitored to lessen their impact. Therefore, identifying chili diseases using a pertinent approach is of enormous importance. Over the years, the growth of computational approaches based on image processing has found its application in automated disease identification, leading to the availability of a reliable monitoring tool that produces promising findings for the chili. Numerous research papers on identifying chili diseases using the approaches have been published. Still, to the best knowledge of the author, there has not been a proper attempt to analyze these papers to describe the many steps of diagnosis, including pre-processing, segmentation, extraction of features, as well as identification techniques. Thus, a total of 50 research paper publications on the identification of chili diseases, with publication dates spanning from 2013 to 2021, are reviewed in this paper. Through the findings in this paper, it becomes feasible to comprehend the development trend for the application of computational approaches based on image processing in the identification of chili diseases, as well as the challenges and future directions that require attention from the present research community.&nbsp

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models

A survey on different plant diseases detection using machine learning techniques

Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer's profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively.Web of Science1117art. no. 264

Automated assessment for early and late blight leaf diseases using extended segmentation and optimized features

Early and late blight diseases lead to substantial damage to vegetable crop productions and economic losses. As a modern solution, machine learning-based plant disease assessment aims to assess the disease incidence and severity through the disease region of interest (ROI) and its extracted features. In the case of existing conventional classifier methods, extracting the features involves generalized ROI segmentation that loosely follows the disease inference. As a result, accuracy is reduced, and the fuzzy boundary region that carries potential properties for improving feature characterization capability is truncated from the ROI. Besides, most of the existing practices extract only the global features, This leads to redundant and extensive feature vector, which causes increased complexity and underperformance. Furthermore, individual lesion severity is not considered in the assessment. This thesis addresses the issue of the ROI segmentation by using color thresholding based on ratios of leaf green color intensity to incorporate the fuzzy boundary region, denoted as extended ROI (EROI). Secondly, the issue of the feature extraction is addressed by the proposed localized feature extraction method to reduce complexity and improve disease classification performance. Based on the color and texture morphological properties of the individual lesions within the EROI, color coherence vector and local binary patterns features are extracted. As a result, a pathologically optimized feature vector is obtained, which is used to build a support vector machine classifier to classify between the disease types of early blight, late blight, and healthy leaves. lastly, a 2-tier assessment is proposed. The disease type classification is given as the first tier, while the leaf lesion area ratios of the individual lesions are given as severity quantification for the second tier. Overall, the proposed EROI segmentation method reduced under-segmentation by up to 80%. The proposed optimized feature reduced the execution run-time by up to 50% and achieved an average classification performance of up to 99%. Finally, the quantified severity is in close agreement with the ground truth by achieving an average accuracy of 93%