![]() Three species-specific PCR primer sets were designed for Alternaria alternata sensu lato, Alternaria solani and Alternaria infectoria identification. ![]() In: The 2020 Indo–Taiwan 2nd International Conference on Computing, Analytics and Networks, pp.Alternaria species are the causal agents of potato and tomato early blight disease. Lee, T.Y., Yu, J.Y., Chang, Y.C., Yang, J.M.: Health detection for potato leaf with convolutional neural network. Rashid, J., Khan, I., Ali, G., Almotiri, S.H., AlGhamdi, M.A., Masood, K.: Multi-level deep learning model for potato leaf disease recognition. (eds.) Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges. Khalifa, N.E.M., Taha, M.H.N., Abou El-Maged, L.M., Hassanien, A.E.: Artificial intelligence in potato leaf disease classification: a deep learning approach. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. ![]() Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., Bhardwaj, S.: Potato leaf diseases detection using deep learning. In: International Conference on Computational Performance Evaluation, pp. ![]() 13(2, 3), 129–134 (2021)īarman, U., Sahu, D., Barman, G.G., Das, J.: Comparative assessment of deep learning to detect the leaf diseases of potato based on data augmentation. Sanjeev, K., Gupta, N.K., Jeberson, W., Paswan, S.: Early prediction of potato leaf diseases using ANN classifier. In: 2020 3rd International Conference on Information and Communications Technology (ICOIACT), pp. Rozaqi, A.J., Sunyoto, A.: Identification of disease in potato leaves using Convolutional Neural Network (CNN) algorithm. Xie, Q., Dai, Z., Hovy, E., Luong, T., Le, Q.: Unsupervised data augmentation for consistency training. Mallah, C., Cope, J., Orwell, J.: Plant leaf classification using probabilistic integration of shape, texture and margin features. In: 2013 International Conference on Control, Computing, Communication and Materials (ICCCCM), pp. Singh, R.G., Kishore, N.: The impact of transformation function on the classification ability of complex valued extreme learning machines. In: Advances in Neural Information Processing Systems, vol. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Jabir, B., Falih, N., Rahmani, K.: Accuracy and efficiency comparison of object detection open-source models. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. In: The 6th International Conference on Optimization and Applications, pp. Jabir, B., Falih, N.: Digital agriculture in Morocco, opportunities and challenges. Gavhale, K.R., Gawande, U.: An overview of the research on plant leaves disease detection using image processing techniques. Tilman, D., Cassman, K.G., Matson, P.A., Naylor, R., Polasky, S.: Agricultural sustainability and intensive production practices. Pretty, J.: Agricultural sustainability: concepts, principles and evidence. The model should be a solution that enables farmers to identify the early blight and late blight diseases present in their potato plants thus they can choose the appropriate treatment. In this work, using deep learning, we propose a model that seeks to classify these potato plant diseases based on convolutional neural networks using one of the most widely used datasets. The treatments for early blight and late blight are different thus it’s important that we should accurately identify what kind of disease is in every potato plant. The early detection of these diseases then the application of the appropriate treatment can prevent economic loss and save a lot of waste. Among them, there are two common diseases known as early blight and late blight. Identifying the various diseases that affect potato plants is a fundamental thing for farmers to avoid losses every year.
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