Plant Leaf Disease Detection Using Image Processing: A Comprehensive Review

Hasan, Md. Nabobi; Mustavi, Mufrad; Jubaer, Md. Abu; Shahriar, Md. Tanvir; Ahmed, Tanvir

doi:10.56532/mjsat.v2i4.80

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Nabobi et al [5] (2022) had examined on the detection of plant leaf diseases, highlighting the limitations of manual visual inspection. They addressed these challenges by utilizing image processing and artificial intelligence algorithms.…”

Section: Literature Surveymentioning

confidence: 99%

An Accurate Plant Disease Detection Technique Using Machine Learning

Raman

2024

EAI Endorsed Trans IoT

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INTRODUCTION: Plant diseases pose a significant threat to agriculture, causing substantial crop and financial losses. Modern technologies enable precise monitoring of plant health and early disease identification. Employing image processing, particularly Convolutional Neural Network (CNN) techniques, allows accurate prediction of plant diseases. The aim is to provide an automated, reliable disease detection system, aiding professionals and farmers in timely action to prevent infections and reduce crop losses. Integrating cutting-edge technologies in agriculture holds vast potential to enhance profitability and production. OBJECTIVES: The primary focus lies in developing an automated system proficient in analysing plant images to detect disease symptoms and classify plants as healthy or disease affected. The system aims to simplify plant disease diagnostics for farmers, providing essential information about leaf name, integrity, and life span. METHODS: The method aims to empower farmers by enabling easy identification of plant diseases, providing essential details like disease name, accuracy level, and life span. The CNN model accurately gauges the system's accuracy level. It further streamlines the process by offering a unified solution through a user-friendly web application, eliminating the need for separate interventions for affected leaves. the system saves farmers time by delivering crucial information directly. RESULTS: The Proposed web application proves to be a comprehensive solution, eliminating the need for farmers to search for separate interventions for affected leaves. The machine learning model exhibits a noteworthy accuracy of 96.67%, emphasizing its proficiency in making correct predictions for the given task. CONCLUSION: In conclusion, the paper successfully employed a CNN algorithm for precise plant disease prediction. With the proposed model deployment, farmers can easily access information about plant diseases, their life span, and preventive measures through the web application. By detecting illnesses early, farmers can promptly take remedial actions to mitigate sicknesses and minimize crop losses. The integrated approach holds promise for advancing agricultural practices and ensuring sustainable crop management.

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Section: Literature Surveymentioning

confidence: 99%

An Accurate Plant Disease Detection Technique Using Machine Learning

Raman

2024

EAI Endorsed Trans IoT

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“…Md. Nabobi Hasan et al [17] used a deep learning-based technique to identify the potato leaf disease and an overall accuracy of 97%.…”

Section: Of 22mentioning

confidence: 99%

An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification

Khatun,

Halder,

Arafat

et al. 2023

Preprint

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Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

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“…Over the last decade, there has been an increase in the number of publications relevant to this field. These can be divided into three categories of forecast models [18]: 1) based on image processing [19][20][21], 2) based on weather data [22][23][24] and, 3) based on distinct types of data coming from heterogeneous sources [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Classification Algorithms for predicting Olive Anthracnose Disease

Kottaridi,

Milionis,

Demopoulos

et al. 2023

Preprint

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Olive Anthracnose (OA) is the most important fungal disease of olive fruits worldwide. In the context of integrated pest management, the development of predictive models could be used for early diagnosis and control. In the current study, a dataset representing 58 cases (6 locations with 12 olive cultivars) was used to study the relationship between ΟΑ incidence (OAI) and 35 heterogeneous variables, including orchard characteristics, olive fruit parameters, foliar and soil nutrients, soil parameters and soil texture classes. The Random Forest-Recursive Feature Elimination with Cross Validation (RF-RFECV) feature selection method identified Location, Water Content, P, Ca, Mg, Exchangeable Mg, Trace Zn, Trace Cu as possible new indicators associated with OAI. Six different classification algorithms, namely Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), were developed for predicting conditions leading to OAI >0% and 10%. Hyperparameter optimization using grid search was used to optimize the parameters of the models and finally the best parameters were applied to predict the OAI. The final models were evaluated in terms of several standard metrics, such as accuracy, sensitivity, specificity and ROC AUC score. Findings suggested that GB performance was superior compared to the other models for the prediction of the occurrence of OA disease (OAI>0%) with an accuracy of 86.7%, a sensitivity of 100%, a specificity of 75% and a ROC-AUC score of 93%, while for the prediction of the spread of the disease (OAI>10%), DT stood out with an accuracy of 86.7%, a sensitivity of 81.8%, a specificity of 100% and a ROC-AUC score of 91%. RF classifier performed very well in both cases, with an accuracy of 80%, a sensitivity of 85.7%, a specificity of 75% and a ROC-AUC score of 93% for the prediction of the occurrence of the disease (OAI>0%), and an accuracy of 86.7%, a sensitivity of 90.9%, a specificity of 75% and a ROC-AUC score of 84% for the prediction of the spread of the disease (OAI>10%).

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Plant Leaf Disease Detection Using Image Processing: A Comprehensive Review

Cited by 8 publications

References 25 publications

An Accurate Plant Disease Detection Technique Using Machine Learning

An Accurate Plant Disease Detection Technique Using Machine Learning

An Efficient Convolution Neural Network Based Novel Framework for Potato Leaf Diseases Classification and Identification

Comparative Analysis of Machine Learning Classification Algorithms for predicting Olive Anthracnose Disease

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