Leaf Disease detection from tomato images using deep learning techniques
Publish place: The 22th National Conference on Computer Science and Engineering and Information Technology
Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: Persian
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شناسه ملی سند علمی:
CECCONF22_014
تاریخ نمایه سازی: 29 تیر 1403
Abstract:
The agricultural industry is vital for providing high-quality food and driving the development of economies and populations. Plant diseases can lead to substantial reductions in food output and the loss of species diversity. Timely detection of plant diseases through precise or automated methods can improve food quality and reduce financial losses. Recent advancements in deep learning have significantly enhanced the accuracy of identifying images and detecting objects. Therefore, our study employed pre-trained models based on convolutional neural networks (CNNs) for effective identification of tomato diseases. The experiments were carried out using the popular PlantVillage dataset in ۱۰ classes. The performance of the model was evaluated through classification accuracy and Our method demonstrated a remarkable ۹۹.۵۸% test accuracy rate, surpassing other works. This confirms the precision of our approach in detecting and classifying diseases in tomato plants.
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Authors
Masoud Yarali Darani
Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.