Transfer Learning based Convolutional Neural Network Model for Classification of Mango Leaves Infected by Anthracnose

Venkatesh,; Nagaraju, Y.; Sahana, Taniya; Swetha, S.; Hegde, Siddhanth U

doi:10.1109/inocon50539.2020.9298269

Cited by 19 publications

(7 citation statements)

References 30 publications

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“…The learning method that involves multilayered representations in artificial neural networks is known as Deep Learning [11]. In agriculture, Deep Learning uses many Convolutional Neural Networks (CNN) in detecting diseases in plants.…”

Section: Deep Learningmentioning

confidence: 99%

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Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

Rinanda,

Aini,

Pertiwi

et al. 2024

PREDATECS

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Plant diseases pose a serious threat to a country's economy and food security. One way to identify diseases in plants is through the visible features on their leaves. Farmers need to conduct an active examination of the condition of the leaves of plants to eradicate this disease. In this case, automatic recognition and classification of diseases of leaf crops is required in order to obtain an accurate identification. Digital image processing technology can be used to solve this problem. One effective approach is the Convolutional Neural Network (CNN). The trial image used a dataset consisting of 4000 images of mango leaf disease, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty Mould. This study aims to compare the accuracy of CNN, VGG16 and InceptionV3. Architectural modeling uses these drawings to train and test models in recognizing and classifying mango leaf diseases. The results of modeling trials in the three scenarios were most optimally obtained by VGG16 with an accuracy of 96.87%, then InceptionV3 with an acquisition of 96.50% and CNN by 81%.

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Section: Deep Learningmentioning

confidence: 99%

“…The classic CNN model is used to perform image recognition and classification [14]. This model consists of several types layers, namely Convolutional Layer, Pooling Layer, and Fully Connected Layer [11]. Convolutional Layer Plays an important role in the extraction of image features.…”

Section: Convolutional Neural Network Modelmentioning

confidence: 99%

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

Rinanda,

Aini,

Pertiwi

et al. 2024

PREDATECS

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“…CNN's can be used to identify plant diseases (3). One of the most effective methods for identifying patterns in large data sets is CNN.…”

Section: Introductionmentioning

confidence: 99%

DenseNet Based Model for Plant Diseases Diagnosis

BAKR

Abdel-Gaber

Nasr

et al. 2022

EJECE

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The biggest threat to the safety of food is plant diseases. They have the ability to dramatically lower the quantity and quality of agricultural products. Recognizing plant diseases is the biggest issue in the agricultural industries. Convolutional Neural Networks (CNN) are effective in solving image classification problems in computer vision. Numerous deep learning architectures have been used to diagnose plant diseases. This study presents a transfer learning-based model for identifying diseases in plant leaves. In this paper, a CNN classifier based on transfer learning model called DenseNet201 are proposed. An analysis of four deep learning models (VGG16, Inception V3, ResNet152V2, and DenseNet201) done to see which one can detect plant diseases with the greatest degree of accuracy. Web based application developed for plant disease diagnosing from defected leaf image and the proposed model which identify the disease and give the recommended treatment. The used images dataset contains 28310 leaves photos of 3 crops, tomato, potato and pepper divided into 15 different classes, 9 disorders and one healthy class for tomato, 2 disorders and one healthy class for potato and 1 disorder and one healthy for pepper. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.44% and validation accuracy of 98.70%.

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“…Plant diseases can be detected using CNNs (Venkatesh et al, 2020). CNN is one of the most powerful pattern identification techniques for massive data sets.…”

Section: Introductionmentioning

confidence: 99%

“…CNN has a really encouraging performance in terms of detecting these disorders. Various CNN classification architectures, VGG16, Inception V3 and DenseNet201 were previously used in diseases detection (Venkatesh et al, 2020;Peyal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Tomato Disease Detection Model Based on Densenet and Transfer Learning

BAKR

AbdelGaber

Nasr

et al. 2022

acs

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Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.

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Transfer Learning based Convolutional Neural Network Model for Classification of Mango Leaves Infected by Anthracnose

Cited by 19 publications

References 30 publications

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

DenseNet Based Model for Plant Diseases Diagnosis

Tomato Disease Detection Model Based on Densenet and Transfer Learning

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