Fruit and Leaves Disease Prediction Using Deep Learning Algorithm
The purpose of Agriculture is not only to feed ever growing population but it’s an important source of energy and a solution to solve the problem of global warming. Plant diseases are extremely significant, as that can adversely affect both quality and quantity of crops in agriculture production. Plant disease diagnosis is very essential in earlier stage in order to cure and control them. Generally the naked eye method is used to identify the diseases. In this method experts are involved who have the ability to detect the changes in leaf color. This method involves lots of efforts, takes long time and also not practical for the large fields. Many times different experts identify the same disease as the different disease. This method is expensive as it requires continuous monitoring of experts. Tree leaves and fruit diseases can increase the cost of agricultural production and may extend to total economic disaster of a producer if not cured appropriately at early stages. The producers need to monitor their crops and detect the first symptoms in order to prevent the spread of a plant disease, with low cost and save the major part of the production. Hiring professional agriculturists may not be affordable especially in remote isolated geographic regions. Machine learning algorithm in image can offer an alternative solution in plant monitoring and such an approach may anyway be controlled by a professional to offer his services with lower cost. It includes image segmentation and image classification approach to predict various types of diseases using Otsu thresholding method and convolutional neural network method.
. S. H. Lee, C. S. Chan, S. J. Mayo, and P. Remagnino, “How deep learning extracts and learns leaf features for plant classification,” Pattern Recognition, vol. 71, pp. 1–13, 2017.
. A. Joly, H. Goëau, P. Bonnet, V. Baki´c, J. Barbe, S. Selmi, I. Yahiaoui, J. Carré, E. Mouysset, J.-F. Molino et al., “Interactive plant identification based on social image data,” Ecological Informatics, vol. 23, pp. 22–34, 2014.
. A. R. Sfar, N. Boujemaa, and D. Geman, “Confidence sets for finegrained categorization and plant species identification,” International Journal of Computer Vision, vol. 111, no. 3, pp. 255–275, 2015.
. J. Chaki, R. Parekh, and S. Bhattacharya, “Plant leaf recognition using texture and shape features with neural classifiers,” Pattern Recognition Letters, vol. 58, pp. 61–68, 2015.
. N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. Soares, “Leafsnap: A computer vision system for automatic plant species identification,” in Computer Vision–ECCV 2012. Springer, 2012, pp. 502–516.
. Y. Naresh and H. Nagendraswamy, “Classification of medicinal plants: An approach using modified lbp with symbolic representation,” Neurocomputing, vol. 173, pp. 1789–1797, 2016.
. G. L. Grinblat, L. C. Uzal, M. G. Larese, and P. M. Granitto, “Deep learning for plant identification using vein morphological patterns,” Computers and Electronics in Agriculture, vol. 127, pp. 418–424, 2016.
. J. Charters, Z. Wang, Z. Chi, A. C. Tsoi, and D. D. Feng, “Eagle: A novel descriptor for identifying plant species using leaf lamina vascular features,” in Multimedia and Expo Workshops (ICMEW), 2014 IEEE International Conference on. IEEE, 2014, pp. 1–6.
. M. G. Larese, R. Namías, R. M. Craviotto, M. R. Arango, C. Gallo, and P. M. Granitto, “Automatic classification of legumes using leaf vein image features,” Pattern Recognition, vol. 47, no. 1, pp. 158–168, 2014.
Copyright (c) 2019 International Research Journal of Multidisciplinary Technovation
This work is licensed under a Creative Commons Attribution 4.0 International License.