TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING

Authors

  • Niharika Saxena M.E. scholar, Electronics and Communication, Ujjain Engineering College, India
  • Dr. Neha Sharma Professor, Electronics and Communication, Ujjain Engineering College, India

DOI:

https://doi.org/10.29121/ijetmr.v9.i6.2022.1177

Keywords:

Artificial Intelligence, Convolutional Neural Network (CNN), Deep Learning, Leaf Disease, Crop Disease, Tomato Leaf

Abstract

Tomatoes are the most extensively planted vegetable crop in India's agricultural lands. Although the tropical environment is favorable for its growth, specific climatic conditions and other variables influence tomato plant growth. In addition to these environmental circumstances and natural disasters, plant disease is a severe agricultural production issue that results in economic loss. Therefore, early illness detection can provide better outcomes than current detection algorithms. As a result, deep learning approaches based on computer vision might be used to detect diseases early. This study thoroughly examines the disease categorization and detection strategies used to identify tomato leaf diseases. The pros and limitations of the approaches provided are also discussed in this study. Finally, employing hybrid deep-learning architecture, this research provides an early disease detection approach for detecting tomato leaf disease.

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Published

2022-06-21

How to Cite

Saxena, N., & Sharma, N. (2022). TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING. International Journal of Engineering Technologies and Management Research, 9(6), 1–14. https://doi.org/10.29121/ijetmr.v9.i6.2022.1177