Study of Leaf Disease using Deep Learning

2019 ◽  
Vol 7 (4) ◽  
pp. 1036-1040
Author(s):  
Sadhvik Reddy ◽  
Saumit Sandesh C ◽  
Srividya KA ◽  
Sandeep Reddy ◽  
Mohana Kumar S ◽  
...  
Keyword(s):  
Deep Learning ◽  
Leaf Disease

Identification of Mango Leaf Disease Using Deep Learning

2021 ◽  
Author(s):  
Nimisha Manoharan ◽  
Vinod J Thomas ◽  
D. Anto Sahaya Dhas
Keyword(s):  
Deep Learning ◽  
Leaf Disease

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.

Plant Leaf Disease Identification and Prescription Suggestion Using Deep Learning

2021 ◽  
pp. 547-560
Author(s):  
P. Y. V. N. Dileep Kumar ◽  
Purnima Singh ◽  
Sagar Pande ◽  
Aditya Khamparia
Keyword(s):  
Deep Learning ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Disease Identification

Maize Leaf Disease Detection and Classification Using Deep Learning

2021 ◽  
pp. 87-102
Author(s):  
Phani Kumar Singamsetty ◽  
G. V. N. D. Sai Prasad ◽  
N. V. Swamy Naidu ◽  
R. Suresh Kumar
Keyword(s):  
Deep Learning ◽  
Disease Detection ◽  
Maize Leaf ◽  
Leaf Disease

Research on deep learning in apple leaf disease recognition

2020 ◽  
Vol 168 ◽  
pp. 105146 ◽  
Author(s):  
Yong Zhong ◽  
Ming Zhao
Keyword(s):  
Deep Learning ◽  
Leaf Disease

scholarly journals Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 95 ◽  
Author(s):  
Kaizhou Li ◽  
Jianhui Lin ◽  
Jinrong Liu ◽  
Yandong Zhao
Keyword(s):  
Deep Learning ◽  
Ginkgo Biloba ◽  
Plant Disease ◽  
Disease Classification ◽  
Field Conditions ◽  
Laboratory Conditions ◽  
Leaf Disease ◽  
And Training ◽  
Ginkgo Leaf

Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learning has made great achievements in plant disease identification and classification. For this paper, the convolution neural network model was used to classify the different degrees of ginkgo leaf disease. This study used the VGGNet-16 and Inception V3 models. After preprocessing and training 1322 original images under laboratory conditions and 2408 original images under field conditions, 98.44% accuracy was achieved under laboratory conditions and 92.19% under field conditions with the VGG model. The Inception V3 model achieved 92.3% accuracy under laboratory conditions and 93.2% under field conditions. Thus, the Inception V3 model structure was more suitable for field conditions. To our knowledge, there is very little research on the classification of different degrees of the same plant disease. The success of this study will have a significant impact on the prediction and early prevention of ginkgo leaf blight.

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