Comparative Assessment of Deep Learning to Detect the Leaf Diseases of Potato based on Data Augmentation

Barman, Utpal; Sahu, Diganto; Barman, Golap Gunjan; Das, Josodhir

doi:10.1109/compe49325.2020.9200015

Cited by 38 publications

(16 citation statements)

References 9 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: 80%

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: 80%

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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“…Barman et al [ 99 ] used a Self-Built Convolutional Neural Network (SBCNN) and MobileNet model for potato leaf disease detection using a dataset that contains 2152 images. Additionally, the PlantVillage open-source dataset was used.…”

Section: Related Workmentioning

confidence: 99%

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

Sinshaw

Assefa

Mohapatra

et al. 2022

Computational Intelligence and Neuroscience

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In most developing countries, the contribution of agriculture to gross domestic product is significant. Plant disease is one of the major factors that adversely affect crop yield. Traditional plant disease detection techniques are time-consuming, biased, and ineffective. Potato is among the top consumed plants in the world, in general, and in developing countries, in particular. However, potato is affected by different kinds of diseases which minimize their yield and quantity. The advancement in AI and machine learning has paved the way for new methods of tackling plant disease detection. This study presents a comprehensive systematic literature review on the major diseases that harm potato crops. In this effort, computer vision-based techniques are employed to identify potato diseases, and types of machine learning algorithms used are surveyed. In this review, 39 primary studies that have provided useful information about the research questions are chosen. Accordingly, the most common potato diseases are found to be late blight, early blight, and bacterial wilt. Furthermore, the review discovered that deep learning algorithms were more frequently used to detect crop diseases than classical machine learning algorithms. Finally, the review categorized the state-of-the-art algorithms and identifies open research problems in the area.

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“…The proposed method was trained and tested on the PlantVillage dataset. Barman et al [36] proposed a self-build CNN (SBCNN) model to detect the early blight, late blight potato leaf diseases, and healthy class. The PlantVillage dataset was also used to train the model, which is for a specific region.…”

Section: Related Workmentioning

confidence: 99%

“…Rozaqi and Sunyoto [34] 6,812,995 92.00% Islam et al [39] -95.00% Sanjeev et al [35] -96.50% Barman et al [36] 16,407,395 96.98% Tiwari et al [37] 143,667,240 97.80% Khalifa et al [33] -98.00% Lee et al [38] 10,089,219 99.00% PDDCNN 8,578,611 99.75%…”

Section: Existing Study Total Parameters Accuracymentioning

confidence: 99%

Multi-Level Deep Learning Model for Potato Leaf Disease Recognition

et al. 2021

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Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region. In this research, a multi-level deep learning model for potato leaf disease recognition has developed. At the first level, it extracts the potato leaves from the potato plant image using the YOLOv5 image segmentation technique. At the second level, a novel deep learning technique has been developed using a convolutional neural network to detect the early blight and late blight potato diseases from potato leaf images. The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. The proposed deep learning technique achieved 99.75% accuracy on the potato leaf disease dataset. The performance of the proposed techniques was also evaluated on the PlantVillage dataset. The proposed technique is also compared with the state-of-the-art models and achieved significantly concerning the accuracy and computational cost.

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Comparative Assessment of Deep Learning to Detect the Leaf Diseases of Potato based on Data Augmentation

Cited by 38 publications

References 9 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

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

Multi-Level Deep Learning Model for Potato Leaf Disease Recognition

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