Role of Image Processing and Machine Learning Techniques in Disease Recognition, Diagnosis and Yield Prediction of Crops: A Review
@article{KP2018RoleOI,
title={Role of Image Processing and Machine Learning Techniques in Disease Recognition, Diagnosis and Yield Prediction of Crops: A Review},
author={Mayuri .K.P},
journal={International Journal of Advanced Research in Computer Science},
year={2018},
volume={9},
pages={788-795}
}Agriculture planning plays a significant growth and
food security of agro-based country like India. In this Review we
present a comprehensive and critical survey on current
challenges and methodologies applied for various image
processing and Machine learning approaches on variety of crops
in their productivity increase, considering the following
measures: Early detection/recognition of crop diseases,
diagnosing methods and crop selection method in yield
prediction. This paper presents… CONTINUE READING
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References
SHOWING 1-10 OF 21 REFERENCES
Recent machine learning based approaches for disease detection and classification of agricultural products
- Computer Science
- 2016 International Conference on Computing Communication Control and automation (ICCUBEA)
- 2016
- 18
- Highly Influential
Crop Selection Method to maximize crop yield rate using machine learning technique
- Mathematics
- 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM)
- 2015
- 76
Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images
- Computer Science
- 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
- 2016
- 45
Detection of plant leaf diseases using image segmentation and soft computing techniques
- Biology
- 2017
- 290
- PDF
An appropriate model predicting pest/diseases of crops using machine learning algorithms
- Computer Science
- 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS)
- 2017
- 15
A survey of image processing techniques for plant extraction and segmentation in the field
- Engineering, Computer Science
- Comput. Electron. Agric.
- 2016
- 172
Unsupervised domain adaptation for early detection of drought stress in hyperspectral images
- Engineering
- 2017
- 11