Advanced Disease Detection Techniques in Plants Using Leaf Disease Detection and Soil’s Nutrient Deficiency

Authors

  • Divya Kumari Student, Department of Information Science & Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India
  • H. J. Bharath Student, Department of Information Science & Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India
  • Jaisal Srivastava Student, Department of Information Science & Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India
  • J. Y. Manvith Student, Department of Information Science & Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India
  • Anusha Preetham Assistant Professor, Department of Information Science & Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, India

Keywords:

Machine Learning, Fertilizer recommendation, Random Forest, Convolutional Neural Networks, SVM, HED

Abstract

Plant disease prediction and detection has long been a major issue in the agricultural field. People with limited financial resources cannot afford to have their crops and fields inspected on a regular basis. As a result, a technique that can meet the needs of poor farmers will be a game changer. The proposed model in this paper has developed a system that can determine whether or not a plant is healthy. Furthermore, if the plan is unhealthy, the reason of the disease is determined by collecting two inputs from the plot where the diseased plant is present: plant leaves and soil samples. The model is taught to determine whether the disease is bacterial, fungal, or viral.

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Published

20-06-2022

How to Cite

[1]
D. Kumari, H. J. Bharath, J. Srivastava, J. Y. Manvith, and A. Preetham, “Advanced Disease Detection Techniques in Plants Using Leaf Disease Detection and Soil’s Nutrient Deficiency”, IJRESM, vol. 5, no. 6, pp. 162–165, Jun. 2022.

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Articles