Tomato Leaf Disease Prediction Using Transfer Learning

2021 ◽  
pp. 3-18
Author(s):  
R. Sangeetha ◽  
M. Mary Shanthi Rani
Keyword(s):  
Transfer Learning ◽  
Tomato Leaf ◽  
Disease Prediction ◽  
Leaf Disease

scholarly journals Tomato leaf disease classification by exploiting transfer learning and feature concatenation

2022 ◽  
Author(s):  
Mehdhar S. A. M. Al‐gaashani ◽  
Fengjun Shang ◽  
Mohammed S. A. Muthanna ◽  
Mashael Khayyat ◽  
Ahmed A. Abd El‐Latif
Keyword(s):  
Transfer Learning ◽  
Tomato Leaf ◽  
Disease Classification ◽  
Leaf Disease

Investigation and comparison of different CNN architectures on tomato leaf disease prediction using deep learning

2021 ◽  
Author(s):  
N. Krishnamoorthy ◽  
K. Nirmaladevi ◽  
S. Shanth ◽  
N. Karthikeyan
Keyword(s):  
Deep Learning ◽  
Tomato Leaf ◽  
Disease Prediction ◽  
Leaf Disease

scholarly journals Senet Cnn Based Tomato Leaf Disease Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 773-777
Keyword(s):  
Neural Network ◽  
High Accuracy ◽  
Tomato Leaf ◽  
Disease Prediction ◽  
Early Prediction ◽  
Potato Plants ◽  
Leaf Disease ◽  
Common Plant ◽  
Layer Concept

Plants play important roles in the environment. Various plants are fulfilling many demands and basic requirements of the society. Saving such entities in the society is the uttermost necessity in today’s world to deal with plant degradations. Many diseases in plants are common and thus they face degradation. While mainly dealing with common plant such as tomato and potato plants, it is observed that they very often face bacterial and other diseases. A proper precaution can be made to save plant from such diseases. Thus the early prediction of such different diseases can be made, which can be a massive savings to the farmer as well as for the country economy. This paper has adapted a moderate different approach of convolutional neural network called SENet. In this approach, a hybrid process is discussed which uses the advantage of SENet and CNN layer concept for better classification. CNN is performed by using the number of layer and kernel selections. Classification of data is performed using the traditional CNN approach. In this scenario, the quick process occurrence is performed using suppression of less used information. It tries to add weight to each and every feature map in the layer. This approach is used to check, identify and detect the defects in leaf of tomato. The prime motive of the presented approach is; to obtain simple easiest method for the detection of disease in tomato leaf with use of minimal computing resources. Thus an improved, efficient algorithm can be made use in real time implementation of leaf disease prediction with high accuracy and efficiency parameters.

scholarly journals Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28822-28831
Author(s):  
Changjian Zhou ◽  
Sihan Zhou ◽  
Jinge Xing ◽  
Jia Song
Keyword(s):  
Tomato Leaf ◽  
Dense Network ◽  
Leaf Disease ◽  
Disease Identification

Transfer Learning based Rice Leaf Disease Classification with Inception-V3

2021 ◽  
Author(s):  
Kotharu Uma Venkata Ravi Teja ◽  
Bhumula Pavan Venkat Reddy ◽  
Likhitha Reddy Kesara ◽  
Kotaru Drona Phani Kowshik ◽  
Lakshmi Anchitha Panchaparvala
Keyword(s):  
Transfer Learning ◽  
Disease Classification ◽  
Rice Leaf ◽  
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.

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