An Efficient Solution for Ensuring Sustainable Agricultural Growth using Artificial Intelligence

  • Chirantan Das University Institute of Technology, The University of Burdwan
  • Kamal Singh University Institute of Technology
  • Sumit Gupta University Institute of Technology
Keywords: Deep Learning, Convolutional Neural Networks, Feature Extraction, Plant Disease Detection, Image Classification


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%.


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How to Cite
Das, C.; Singh, K.; Gupta, S. Agrijaanch. prepare@u_foset 2022.
12th Inter-University Engineering, Science & Technology Academic Meet – 2022