Agrijaanch
An Efficient Solution for Ensuring Sustainable Agricultural Growth using Artificial Intelligence
Abstract
With the advancement in agricultural technology and the use of Artificial Intelligence in diagnosing plant diseases, it becomes important to make pertinent research for sustainable agricultural development. We have proposed and built a Web-based system to spread awareness amongst Farmers and Agro-companies about various diseases that plants are infected with and also their possible remedies. Different diseases have different causes and thus different solutions and mitigation strategies need to be adopted. If the diseases are misjudged or mismanaged, then they can get spread profusely and wreck havoc on the soil. So we came up with a potential approach for effective, efficient and automated detection of the prevalent diseases during the budding phase of the agricultural products to assist the farmers in employing preventive measure on time. We have developed a set of both Machine Learning Models and Deep Learning Models to detect diseases in the potato plant. The models are deployed on our website and when the image of a Potato plant is uploaded, the result that whether there is any disease in the plant or not will be generated. Amongst all models trained and tested in this work, Convolutional Neural Network yields the best classification accuracy of 99.30%.
Downloads
References
Athanikar, G., & Badar, P. (2016). Potato leaf diseases detection and classification system. International Journal of Computer Science and Mobile Computing, 5(2), 76-88.
Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393.
Deepa, S., & Umarani, R. (2017). Steganalysis on Images using SVM with Selected Hybrid Features of Gini Index Feature Selection Algorithm. International Journal of Advanced Research in Computer Science, 8(5).
FAOSTAT (2020). Compare data. Production - Crops and livestock products. https://www.fao.org/faostat/en/#compare
Guettari, N., Capelle-Laizé, A. S., & Carré, P. (2016, September). Blind image steganalysis based on evidential k-nearest neighbors. In 2016 IEEE international conference on image processing (ICIP) (pp. 2742-2746). IEEE.
Huang, H., & Dasgupta, A. (2018, April). Mechanistic model for the stress-strain response of double-layered PSA. In 2018 19th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) (pp. 1-5). IEEE.
Islam, M., Dinh, A., Wahid, K., & Bhowmik, P. (2017, April). Detection of potato diseases using image segmentation and multiclass support vector machine. In 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE) (pp. 1-4). IEEE.
Kabir, M. M., Ohi, A. Q., & Mridha, M. F. (2021). A multi-plant disease diagnosis method using convolutional neural network. In Computer Vision and Machine Learning in Agriculture (pp. 99-111). Springer, Singapore.
Kawasaki, Y., Uga, H., Kagiwada, S., & Iyatomi, H. (2015, December). Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In International symposium on visual computing (pp. 638-645). Springer, Cham.
Kodovsky, J., Fridrich, J., & Holub, V. (2011). Ensemble classifiers for steganalysis of digital media. IEEE Transactions on Information Forensics and Security, 7(2), 432-444.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Li, Y., & Wu, H. (2012). A clustering method based on K-means algorithm. Physics Procedia, 25, 1104-1109.
Sharma, P., Berwal, Y. P. S., & Ghai, W. (2020). Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Information Processing in Agriculture, 7(4), 566-574.
Sheikhan, M., Pezhmanpour, M., & Moin, M. S. (2012). Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks. Neural Computing and Applications, 21(7), 1717-1728.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017, February). Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI conference on artificial intelligence.
Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., & Bhardwaj, S. (2020, May). Potato leaf diseases detection using deep learning. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 461-466). IEEE.
