Code:-
JPM2306
Abstract:-
With the development of plant phenomics, the identification of plant diseases from leaf images has become an effective and economic approach in plant disease science. Among the methods of plant diseases identification, the convolutional neural network (CNN) is the most popular one for its superior performance. However, CNN�s representation power is still a challenge in dealing with small datasets, which greatly affects its popularization. In this work, we propose a new method, namely PiTLiD, based on pretrained Inception-V3 convolutional neural network and transfer learning to identify plant leaf diseases from phenotype data of plant leaf with small sample size. To evaluate the robustness of the proposed method, the experiments on several datasets with small-scale samples were implemented. The results show that PiTLiD performs better than compared methods. This study provides a plant disease identification tool based on a deep learning algorithm for plant phenomics.
Hardware Requirements:-
- System : Pentium i3 Processor.</li> <li>Hard Disk : 500 GB.</li> <li>Monitor : 15�� LED.</li> <li>Input Devices : Keyboard, Mouse.</li> <li>Ram : 8 GB.</li>
Software Requirements:-
- Operating system : Windows 10 Pro.</li> <li>Coding Language : MATLAB</li> <li>Tool : MATLABR2021A</li>
Reference:-
- Kangchen Liu and Xiujun Zhang, �PiTLiD: Identification of Plant Disease From Leaf Images Based on Convolutional Neural Network�, IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGYAND BIOINFORMATICS, VOL. 20, NO. 2, MARCH/APRIL 2023.</p>
Cost:-
Rs 3000
Algorithm / Model Used
AlexNet CNN Model.
Identification of Plant Disease from Leaf Images Based on Convolutional Neural Network
Code:
JPM2306
Abstract:
With the development of plant phenomics, the identification of plant diseases from leaf images has become an effective and economic approach in plant disease science. Among the methods of plant diseases identification, the convolutional neural network (CNN) is the most popular one for its superior performance. However, CNN�s representation power is still a challenge in dealing with small datasets, which greatly affects its popularization. In this work, we propose a new method, namely PiTLiD, based on pretrained Inception-V3 convolutional neural network and transfer learning to identify plant leaf diseases from phenotype data of plant leaf with small sample size. To evaluate the robustness of the proposed method, the experiments on several datasets with small-scale samples were implemented. The results show that PiTLiD performs better than compared methods. This study provides a plant disease identification tool based on a deep learning algorithm for plant phenomics.
Hardware Requirements:
- System : Pentium i3 Processor.</li> <li>Hard Disk : 500 GB.</li> <li>Monitor : 15�� LED.</li> <li>Input Devices : Keyboard, Mouse.</li> <li>Ram : 8 GB.</li>
Software Requirements:
- Operating system : Windows 10 Pro.</li> <li>Coding Language : MATLAB</li> <li>Tool : MATLABR2021A</li>
Reference:
- Kangchen Liu and Xiujun Zhang, �PiTLiD: Identification of Plant Disease From Leaf Images Based on Convolutional Neural Network�, IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGYAND BIOINFORMATICS, VOL. 20, NO. 2, MARCH/APRIL 2023.</p>
Cost:
Rs 3000
Additional Information
Algorithm / Model Used:
AlexNet CNN Model.
Tools Used:
MATLAB
Cost:
₹Rs 3000
Category:
MATLAB
