Agricultural Plant Leaf Disease Detection Using Deep Learning Techniques

2021 ◽  
Author(s):  
Jashraj Karnik ◽  
Anil Suthar
Keyword(s):  
Deep Learning ◽  
Disease Detection ◽  
Plant Leaf ◽  
Agricultural Plant ◽  
Leaf Disease ◽  
Learning Techniques

scholarly journals Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model

2021 ◽  
Author(s):  
Hepzibah Elizabeth David ◽  
K. Ramalakshmi ◽  
R. Venkatesan ◽  
G. Hemalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Computational Effort ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Tomato Production ◽  
Classification Image ◽  
Leaf Disease ◽  
Learning Techniques

Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.

Performance of deep learning vs machine learning in plant leaf disease detection

2021 ◽  
Vol 80 ◽  
pp. 103615
Author(s):  
R. Sujatha ◽  
Jyotir Moy Chatterjee ◽  
NZ Jhanjhi ◽  
Sarfraz Nawaz Brohi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease

Tomato Leaf Disease Detection Using Deep Learning Techniques

2020 ◽  
Author(s):  
Surampalli Ashok ◽  
Gemini Kishore ◽  
Velpula Rajesh ◽  
S. Suchitra ◽  
S.G. Gino Sophia ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Leaf Disease ◽  
Learning Techniques

scholarly journals Computer Vision-based Plant Leaf Disease Recognition using Deep Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 622-626
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Leaf Spot ◽  
Vital Role ◽  
Cercospora Leaf Spot ◽  
Science And Engineering ◽  
Plant Leaf ◽  
Agricultural Improvement ◽  
Leaf Disease ◽  
Learning Techniques

Computer vision-based applications play a vital role in the era of computer science and engineering. Now-a-days peoples are facing different problems in agricultural fields to improve their cultivation. So, a better approach is proposed for plant leaf disease recognition using deep learning techniques for agricultural improvement. This research is very much helpful for the farmers to identify the leaf diseases of a plant. This proposed system has three subsections. One is feature extraction, second is trained networking generation and the third one is classification. This system first takes an image as the input and extracts the features from the image using K-means clustering. Secondly, it generates a trained network using Convolutional Neural Networks (CNNs). Then compare the original leaf image with the generated trained database in the classification section and recognition of the disease of the plant. Different techniques are used in this system for properly recognized the diseases. After analyzed the 3000 trained images, three types of leaf diseases are properly recognized by this system, which are Cercospora Leaf Spot, Mosaic virus, and Alternaria Leaf Spot. The overall accuracy of this system is very good and which is up to 95.26%.

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