Shurtleff, M.C., Pelczar, M.J., Kelman, A., Pelczar, R.M. The Food and Agriculture Organization of the United Nations (FAO) estimates that pests and diseases lead to the loss of 20–40% of global food production, constituting a threat to food security (Food and Agriculture Organization of the United Nation, International Plant Protection Convention, 2017). There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. © 2020 Springer Nature Switzerland AG. Over 10 million scientific documents at your fingertips. Appl. The disease classification accuracy achieved by the proposed architecture is up to 95.81% and various observations were made with different hyperparameters of the CNN architecture. IEEE Access, © Springer Nature Singapore Pte Ltd. 2020, Emerging Technology Trends in Electronics, Communication and Networking, International Conference on Emerging Technology Trends in Electronics Communication and Networking, http://www.fao.org/3/ca4887en/ca4887en.pdf, https://www.britannica.com/science/plant-disease, Department of Electronics and Communication Engineering, Institute of Technology, https://doi.org/10.1007/978-981-15-7219-7_23, Communications in Computer and Information Science. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), pp. Ferentinos, K.P. Abstract: Crop diseases are a noteworthy risk to sustenance security, however their quick distinguishing proof stays troublesome in numerous parts of the world because of the non attendance of the important foundation. In this paper, a Convolutional Neural Network (CNN) architecture for plant leaf disease detection using techniques of Deep Learning is proposed. REFERENCES India is the second larger producer of wheat after china. Email - aiworksprojects@gmail.com We are always open to all project prospects. However, food security remains threatened by a number of factors including climate change (Tai et al., 2014), the decline in pollinators (Report of the Plenary of the Intergovernmental Science-PolicyPlatform on Biodiversity Ecosystem and Services on the work of its fourth session, 2016), plant dise… Its impact is found in Alabama, Georgia parts of Southern US. IEEE Access. It is important to develop the requisite infrastructure and tools for the detection of diseases in crops. If playback doesn't begin shortly, try restarting your device. Various diseases damage the chlorophyll of leaves and affect with brown or black marks on the leaf area. Computer vision and machine learning techniques have been applied to different disease detection such as tomatoes, grapes, potatoes and cotton. Implementation was done in Matlab using deep learning toolbox. This paper is highlighting the outliers about the wheat leaf disease detection. Their use has been one of the factors behind the increase in food … Lu, J., Hu, J., Zhao, G., Mei, F., Zhang, C.: An in-field automatic wheat disease diagnosis system. Eng. Geetharamani, G., Pandian, A.: Identification of plant leaf diseases using a nine-layer deep convolutional neural network. In this paper, convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaves images … Moti- AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. In: BTW (Workshops), pp. Nikola, M., Trendov, S.V., Zeng, M.: Digital technologies in agriculture and rural areas briefing paper (2019). leafdetectionALLsametype.py for running on one same category of images (say, all images are infected) and leafdetectionALLmix.py for creating dataset for both category (infected/healthy) of leaf images, in the working directory.Note: The code is set to run for all .jpg,.jpeg and .png file format images only, present in the specified directory.