Application of MobileNet-v1 for Potato Plant Disease Detection Using Transfer Learning

2021 ◽  
Author(s):  
Sumita Mishra ◽  
Anshuman Singh ◽  
Vineet Singh
Keyword(s):  
Transfer Learning ◽  
Potato Plant ◽  
Plant Disease ◽  
Disease Detection

scholarly journals Transfer learning-based Plant Disease Detection

2021 ◽  
pp. 469-477
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Transfer Learning ◽  
Plant Disease ◽  
Deep Neural Networks ◽  
Main Tool ◽  
Classification Problem ◽  
Disease Detection ◽  
Classification Problems ◽  
Crop Species

Deep Neural Networks in the field of Machine Learning (ML) are broadly used for deep learning. Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification problems. Deep neural networks have been highly successful in image classification problems. In this paper, we have shown the use of deep neural networks for plant disease detection, through image classification. This study provides a transfer learning-based solution for detecting multiple diseases in several plant varieties using simple leaf images of healthy and diseased plants taken from PlantVillage dataset. We have addressed a multi-class classification problem in which the models were trained, validated and tested using 11,333 images from 10 different classes containing 2 crop species and 8 diseases. Six different CNN architectures VGG16, InceptionV3, Xception, Resnet50, MobileNet, and DenseNet121 are compared. We found that DenseNet121 achieves best accuracy of 95.48 on test data.

scholarly journals Potato Leaf Disease Classification using Transfer Learning based Modified Xception Model

2020 ◽  
pp. 438-442
Author(s):  
Rajasekaran Thangaraj ◽  
Pandiyan P ◽  
Vishnu Kumar Kaliappan ◽  
Anandamurugan S ◽  
Indupriya P
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Potato Plant ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Selection Criterion ◽  
Plant Diseases ◽  
Disease Classification ◽  
Agricultural Field ◽  
Potato Leaf

Plant diseases are the essential thing which decreases the quantity as well quality in agricultural field. As a result, the identification and analysis of the diseases are important. The proper classification with least data in deep learning is the most challenging task. In addition, it is tough to label the data manually depending upon the selection criterion. Transfer learning algorithm helps in resolving this kind of problem by means of learning the previous task and then applying capabilities and knowledge to the new task. This work presents the convolution neural network-based model to predict and analysis the potato plant disease using plant village datasets with deep learning algorithms. Transfer learning with feature extraction model is employed to detect the potato plant disease. The results show that improved performance with an accuracy of 98.16%, precision of 98.18%, the recall value of 98.17% and the F1 score value of 98.169 %.

scholarly journals Deep Transfer Learning Based Rice Plant Disease Detection Model

2022 ◽  
Vol 31 (2) ◽  
pp. 1257-1271
Author(s):  
R. P. Narmadha ◽  
N. Sengottaiyan ◽  
R. J. Kavitha
Keyword(s):  
Transfer Learning ◽  
Rice Plant ◽  
Plant Disease ◽  
Disease Detection ◽  
Detection Model

scholarly journals Plant Diseases Detection and Pesticide Suggestion Implementation

2021 ◽  
Vol 9 (VI) ◽  
pp. 1806-1812
Author(s):  
Onkar Kunjir
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Transfer Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Detection ◽  
Plant Leaf

Plant diseases affect the life of not only farmers but also businesses which are dependent on it. Plant disease detection is a computer vision problem which tries to identify the disease splat is infected using an image of a plant leaf. Different kinds of models have been proposed to tackle this problem. This paper focuses on generating small, lightweight and accurate models with the help of deep learning and transfer learning.

Tomato plant disease detection using transfer learning with C-GAN synthetic images

2021 ◽  
Vol 187 ◽  
pp. 106279
Author(s):  
Amreen Abbas ◽  
Sweta Jain ◽  
Mahesh Gour ◽  
Swetha Vankudothu
Keyword(s):  
Transfer Learning ◽  
Tomato Plant ◽  
Plant Disease ◽  
Disease Detection ◽  
Synthetic Images

scholarly journals Disease Detection using CNN and Transfer Learning

2021 ◽  
pp. 617-621
Author(s):  
Sachin Kumar Jha ◽  
Aditya Raj ◽  
Rahul Kumar ◽  
Vikash Vishal
Keyword(s):  
Machine Learning ◽  
Urban Area ◽  
Rural Area ◽  
Transfer Learning ◽  
Rural Areas ◽  
Plant Disease ◽  
Fruits And Vegetables ◽  
Disease Detection ◽  
Proposed Model ◽  
Learning Technique

Plant disease have great affect over the productivity of fruits and vegetables in rural area. It is estimated that pest and diseases cause loss of 20% crops in rural area vs 10% in urban area. One of the main reasons of this loss is lack of knowledge to identity the disease, its cause and its treatment. To overcome this problem in most economical way, we have used machine learning to identify the disease and suggest remedies to the farmers in rural areas. The proposed model simply accepts an image of leaf with unknown disease, identify the disease and suggest remedies. In this model we are using transfer learning technique to train our model in least amount of time over relatively smaller data. We have achieved up to 98.8% accuracy in identifying 38 different diseases among 14 different fruits and vegetables.

scholarly journals On Using Transfer Learning For Plant Disease Detection

2020 ◽  
Author(s):  
Abhinav Sagar ◽  
J Dheeba
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Transfer Learning ◽  
Plant Disease ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Disease Detection ◽  
Classification Problems ◽  
Test Set ◽  
Multi Class Classification

ABSTRACTDeep neural networks have been highly successful in image classification problems. In this paper, we show how deep neural networks can be used for plant disease recognition in the context of image classification. We have used a publicly available Plant Village dataset which has 38 classes of diseases. Hence the problem that we have addressed is a multi class classification problem. We have compared five different architectures including VGG16, ResNet50, InceptionV3, InceptionResNet and DenseNet169 as the backbones for our work. We found that ResNet50 which uses skip connections using a residual layer archives the best result on the test set. For evaluating the results, we have used metrics like accuracy, precision, recall, F1 score and class wise confusion metric. Our model achieves the best of results using ResNet50 with accuracy of 0.982, precision of 0.94, recall of 0.94 and F1 score of 0.94.

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