Detection of Leaf Diseases Using Deep Learning

Authors

  • M. Madheswaran Professor,
  • J. Abinayashree UG Student
  • F. Jannathul Firthous UG Student

Abstract

Agriculture, the backbone of India, is declining due to many reasons, one of the main of which is that plants are affected by diseases. Potatoes and tomatoes are also important plants and seeds rich in vitamin C and potassium, which are very useful for improving the health and productivity of food products. In this work, we use Mobile Net V3 architecture and neural networks to detect potato and tomato leaf diseases. In this process, data is collected from the Kaggle dataset. The network includes three classes early blights, late blights and healthy conditions of the leaves which works on TensorFlow platform. In this work Convolutional Deep neural network helps the model to learn using various layers based on looping process. It uses the multiclass classification. This paper proposes of various modules which includes (i) Pre-processing module (ii) Normalization (iii) Layers (iv) Optimizer (v) Model fit (v) E-poach. In future, it will help the farmers to identify the right diseases at an early stage which will be very helpful in taking the right measures and using the right insecticides to recover the leaves.

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Author Biographies

M. Madheswaran, Professor,

Department of Electronics and Communication Engineering Muthayammal Engineering College–Rasipuram, Namakkal (D.t), Tamil Nadu-INDIA

J. Abinayashree, UG Student

Department of Electronics and Communication Engineering Muthayammal Engineering College–Rasipuram, Namakkal (D.t), Tamil Nadu-INDIA

F. Jannathul Firthous, UG Student

Department of Electronics and Communication Engineering Muthayammal Engineering College–Rasipuram, Namakkal (D.t), Tamil Nadu-INDIA

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Published

2023-05-23

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