Plant Leaf Disease Detection using Image Processing

Kundu, Rahul; Chauhan, Usha; Chauhan, Sps

doi:10.1109/iciptm54933.2022.9754170

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…Rahul et al [7] (2022) had described the significance of India's agriculture industry. Their research study demonstrated methods for identifying plant illnesses using image processing in leaves in an effort to provide a solution to the query of whether the grains and crops are chemical-free and healthy.…”

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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“…The year 2012 marked a significant milestone in the field of computer vision when Alex Krizhevesky and his team introduced a novel CNN model that was deeper and wider than the existing LeNet architecture [33,35,42,51,52]. This new model was put to the test in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and emerged victorious, demonstrating its superior ability to recognize visual objects.…”

Section: Alexnetmentioning

confidence: 99%

“…The AlexNet architecture is a CNN that was specifically developed to process high-resolution RGB images with dimensions of 224 × 224 × 3. The model consists of a total of 62 million trainable parameters [33,35,42,51,52].…”

Section: Alexnetmentioning

confidence: 99%

An Overview of Recent Advances in Greenhouse Strawberry Cultivation Using Deep Learning Techniques: A Review for Strawberry Practitioners

Yang,

Kim

2023

Agronomy

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Strawberry (Fragaria × ananassa Duch.) has been widely accepted as the “Queen of Fruits”. It has been identified as having high levels of vitamin C and antioxidants that are beneficial for maintaining cardiovascular health and maintaining blood sugar levels. The implementation of advanced techniques like precision agriculture (PA) is crucial for enhancing production compared to conventional farming methods. In recent years, the successful application of deep learning models was represented by convolutional neural networks (CNNs) in a variety of disciplines of computer vision (CV). Due to the dearth of a comprehensive and detailed discussion on the application of deep learning to strawberry cultivation, a particular review of recent technologies is needed. This paper provides an overview of recent advancements in strawberry cultivation utilizing Deep Learning (DL) techniques. It provides a comprehensive understanding of the most up-to-date techniques and methodologies used in this field by examining recent research. It also discusses the recent advanced variants of the DL model, along with a fundamental overview of CNN architecture. In addition, techniques for fine-tuning DL models have been covered. Besides, various strawberry-planting-related datasets were examined in the literature, and the limitations of using research models for real-time research have been discussed.

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Detection of Pepper Leaf Diseases Through Image Analysis Using Radial Basis Function Neural Networks

Rusliyawati,

Karnadi,

Tanniewa

et al. 2024

BIO Web Conf.

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Pepper (Piper nigrum L.) is a high-value cash crop and plays a significant role in Indonesia's agricultural sector. However, pepper production is often hindered by diseases that affect the plant's leaves. This study aims to develop a pepper leaf disease detection model based on image analysis using a Radial Basis Function Neural Network (RBFNN). Conventional methods relying on expert visual assessment are often inefficient, especially on a large scale. In this research, image preprocessing was performed by transforming the images into the CIELAB color space and using K-Means Clustering for feature extraction. Texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM) provides rich information about patterns and intensity distribution in the images, which is effective for distinguishing disease classes. The RBFNN algorithm is then used to identify diseases by capturing the complex non-linearities in the data. Based on the testing results, this model achieved an accuracy rate of 91.67%, demonstrating excellent performance.

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Plant Leaf Disease Detection using Image Processing

Cited by 9 publications

References 5 publications

An Accurate Plant Disease Detection Technique Using Machine Learning

An Accurate Plant Disease Detection Technique Using Machine Learning

An Overview of Recent Advances in Greenhouse Strawberry Cultivation Using Deep Learning Techniques: A Review for Strawberry Practitioners

Detection of Pepper Leaf Diseases Through Image Analysis Using Radial Basis Function Neural Networks

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