Survey Paper on Detection of Unhealthy Region of Plant Leaves Using Image Processing and Soft Computing Techniques

Butale, Namita M.; Kodavade, D. V.

doi:10.22362/ijcert/2018/v5/i12/v5i1204

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…It is worth noting that previous research primarily employed Grid Search for hyperparameter optimization, which is an exhaustive search method. Our literature review revealed a wealth of research papers dedicated to plant disease detection using various techniques [10], [11], [12], [13] This section of our paper underscored the significance of these valuable studies within the context of CNN models. The subsequent section provided an overview of different CNN architectures applied to plant disease diagnosis.…”

Section: Related Workmentioning

confidence: 82%

Plant Disease Management: A Fine-Tuned Enhanced CNN Approach with Mobile App Integration for Early Detection and Classification

Iftikhar,

Kandhro,

Kausar

et al. 2023

Preprint

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Farmers face a daunting challenge in meeting the escalating demands of a rapidly growing population for agricultural products, while plant diseases continue to exact a devastating toll on food production. Despite investing billions of dollars in disease management, agriculturists often struggle to achieve effective disease control without the support of advanced technology. The article explores a deep learning-based approach for disease detection. Specifically, it employs a Convolutional Neural Network (CNN) architecture for the detection. For the automated detection of plant disease, using plant images. This paper presents a new model for the early detection of plant detection based on processing plant images. And compare the in-depth performance analysis of hyper parameters in the context of plant disease detection by focusing on three distinct crops: (Apple, Corn, and Potato). Moreover, the data augmentation impact is analyzed. To enhance accessibility for farmers, our model is seamlessly integrated with a mobile application. The experimental results show the efficiency of our fine-tuned enhanced CNN model (E-CNN) achieving 98.17% accuracy on fungal classes. This research endeavors to pave the way for more effective plant disease management and ultimately to improve agricultural productivity in the face of mounting global challenges.

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Section: Related Workmentioning

confidence: 82%

Plant Disease Management: A Fine-Tuned Enhanced CNN Approach with Mobile App Integration for Early Detection and Classification

Iftikhar,

Kandhro,

Kausar

et al. 2023

Preprint

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show abstract

“…As the world population is projected to reach 9.7 billion by 2050, efficient and effective farming practices are becoming increasingly essential [1]. However, the agricultural sector faces significant challenges, primarily due to plant diseases caused by fungi, viruses, and bacteria [2]. The accurate identification and diagnosis of plant infections are critical, as incorrect diagnoses can lead to decreased resistance in plants and reduced crop yields.…”

Section: Introductionmentioning

confidence: 99%

Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm

2024

Int. Res. J. multidiscip. Technovation

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Agriculture plays a vital role in India's economy, and the health of crops is critical for maximizing yield. In particular, cucumber, a key salad ingredient known for its health benefits, is susceptible to various diseases such as water mold, bacterial wilt, angular leaf spot, anthracnose, and powdery mildew. These diseases not only affect the quality of cucumbers but also significantly reduce their yield. Early detection of these diseases is crucial for successful cultivation, but traditional manual methods of disease identification by farmers or diagnosticians are time-consuming and prone to misidentification. To address these challenges, we explore advanced artificial intelligence techniques. We implement and compare various machine learning algorithms, including ResNet, AlexNet, and VGG-16, for disease classification in cucumbers. However, these methods often struggle with issues such as noise, irrelevant features, and the generation of pertinent characteristics. To overcome these limitations, we propose a novel approach using a GLCM (Gray Level Co-occurrence Matrix) feature extraction method combined with a Random Forest classifier. This new algorithm aims to improve the accuracy and efficiency of disease detection. Our dataset comprises four distinct categories: Healthy, Anthracnose, Aphids, and CYSDV. It is sourced from diverse platforms, including online repositories like kaggle and direct collection from cucumber farms. The initial phase of our methodology involves noise reduction by converting images into the LAB color space and isolating specific regions using the k-means clustering algorithm. Subsequently, we extract texture features from the diseased leaf images using the GLCM algorithm, and classification is performed using the Random Forest model. Comparative analysis shows that our proposed Random Forest algorithm outperforms previous models like LGBM (Light Gradient Boosting Machine) and QSVM (Quantum-Support Vector Machine) in predicting disease presence in cucumber plants with higher accuracy rate of 98.62%, Precision 98.77%, Recall 98.48% and also F1 Score 98.62%.

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Automated Disease Detection and Classification of Plants Using Image Processing Approaches: A Review

Shashi

Singh

2021

Proceedings of Second International Conference on Computing, Communications, and Cyber-Security

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Survey Paper on Detection of Unhealthy Region of Plant Leaves Using Image Processing and Soft Computing Techniques

Cited by 5 publications

References 2 publications

Plant Disease Management: A Fine-Tuned Enhanced CNN Approach with Mobile App Integration for Early Detection and Classification

Plant Disease Management: A Fine-Tuned Enhanced CNN Approach with Mobile App Integration for Early Detection and Classification

Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm

Automated Disease Detection and Classification of Plants Using Image Processing Approaches: A Review

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