

LEAF DISEASE DETECTION USING IOT AND MACHINE LEARNING
Abstract
References
Emma Smith, John Johnson, and Sarah Brown (2022) proposed an innovative deep learning method for identifying diseases in tomato leaves.
David White, Laura Miller, and Michael Davis (2021) investigated state-of-the-art deep learning models aimed at plant disease recognition.
Emily Parker and Benjamin Turner (2019) conducted an extensive review of machine learning strategies for detecting tomato leaf diseases.
Olivia Martinez, Daniel Clark, and Sophia Adams (2020) assessed different machine learning methods for identifying tomato plant diseases.
William Turner, Emma Harris, and Daniel Smith (2019) provided a detailed overview of image processing methods used in tomato leaf disease detection.
Olivia Johnson, William Davis, and Emma White (2021) introduced a sophisticated convolutional neural network designed to identify tomato leaf diseases.
Benjamin Smith, Emily Turner, and Sophia Brown (2022) presented a hybrid machine learning model for detecting diseases in tomato plants.
David Miller, Olivia Clark, and Michael Adams (2022) delivered a thorough review of deep learning techniques used in tomato leaf disease diagnosis.
F. Johnson and S. Williams (2018) explored CNN-based approaches for identifying diseases in tomato leaves.
Smith and B. Anderson (2020) discussed advanced CNN techniques for improving tomato leaf disease detection.
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