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 Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 939 ◽  
Author(s):  
Marko Arsenovic ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Darko Stefanovic
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Real Life ◽  
Weather Conditions ◽  
Plant Diseases ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Disease Detection ◽  
Single Leaf ◽  
The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

scholarly journals Predicting Groundnut Disease using CNN Models

2021 ◽  
Vol 23 (06) ◽  
pp. 756-766
Author(s):  
Neha Suresh ◽  
◽  
Dr.AnandiGiridharan Dr.AnandiGiridharan ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Arachis Hypogaea ◽  
Efficient Method ◽  
Learning Process ◽  
Plant Disease ◽  
Disease Classification ◽  
Agricultural Field ◽  
Arachis Hypogaea L ◽  
Overall Performance

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. Groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root, and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of convolutional neural network (CNN) because it automatically detects the important features without any human supervision. The proposed methodology can deeply detect plant disease by using a deep learning process. Ultimately, the groundnut disease classification with its overall performance of the proposed methodology provides 96% accuracy.

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.

scholarly journals Plant Disease Classification using Deep Learning Google Net Model

2019 ◽  
Vol 8 (9S) ◽  
pp. 319-322
Keyword(s):  
Deep Learning ◽  
Food Production ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Classification ◽  
Disease Detection ◽  
Crop Varieties ◽  
Growing Age ◽  
Crop Types ◽  
Harvesting Stage

Plant diseases have been a major crisis that is disturbing the food production. So there is a need to provide proper procedures for plant disease detection at its growing age and also during harvesting stage. Timely disease detection can help the user to respond instantly and sketch for some defensive actions. This detection can be carried out without human intervention by using plant leaf images. Deep learning is progressively best for image detection and classification. In this effort, a deep learning based GoogleNet architecture is used for plant diseases detection. The model is trained using public database of 54,306 images of 14 crop varieties and their respective diseases. It achieves 97.82% accuracy for 14 crop types making it capable of further deployment in a crop detection and protection application.

scholarly journals Automated Plant Disease Detection using Deep Learning Architectures with Autonomous rover

2020 ◽  
Vol 9 (2) ◽  
pp. 248-254
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Vital Role ◽  
Plant Diseases ◽  
Asian Countries ◽  
Agricultural Produce ◽  
Underlying Principle ◽  
Learning Architectures ◽  
The Internet Of Things ◽  
Identification Mechanism

Agriculture is the backbone and plays a vital role in many Asian countries. Farmers mainly depend on their agricultural produce for their living. A report says one-third of the farmers income account’s for the agricultural loss which is primarily due to plant diseases. To combat this farmers are in need of a early plant disease identification mechanism. Observation of individual plants in the farm for detecting the disease is labor-intensive and time consuming work, if the farm is vast and multiple plants are cultivated then it’s even worse. To solve such issues, current technologies like the Internet of Things (IoT) and artificial intelligence (AI) and Machine Learning (ML) are used to predict the diseases more effectively. Farmers usually detect plant diseases with the help of images captured manually and analyzed separately by experts. The proposed system renders an efficient solution for detecting multiple diseases in several plant varieties. The system is designed to detect and recognize several plant varieties, specifically pepper, grapes, and strawberry. The proposed system discovers various plant’s various diseases based on the inputs obtained by capturing images from a built-in camera present in the Autonomous rover. The rover also record’s it’s GPS location and makes a map of the entire farm traced and checked by the robot. The images are processed and are classified into their respective categories using deep learning algorithms. Convolutional neural networks the powerful methodology for image classification is the underlying principle applied. The deep learning model’s architecture namely, VGG16 and InceptionResNetV2, are used to train the model. These models are primarily made of convolutional layers. On testing, we recorded am accuracy of 93.21% was obtained from VGG16, and 95.24% from InceptionResNetV2.

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