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LEAF DISEASE DETECTION USING IOT AND MACHINE LEARNING

Prerana Jadhav, Ankita ., Rajeshwari ., Seeta ., Mahananda R. Hatti

Abstract


Agriculture plays a vital role in crop production and serves as a primary means of livelihood for a significant portion of the population, particularly in India. As the backbone of a country’s economy, agriculture not only supports food production but also generates widespread employment opportunities. The health of crops is crucial for farmers to ensure higher productivity and profitability. Monitoring plant health throughout different growth stages is essential to prevent various plant diseases that could negatively impact crop yields. Currently, traditional crop monitoring relies heavily on manual observation, which is time-consuming and less efficient. Therefore, there is an increasing demand for automated plant disease detection systems that can identify issues at an early stage. To address this, many disease tracking methods have been implemented by farmers at regular intervals. A more advanced approach combines the capabilities of Internet of Things (IoT)-based monitoring systems with Machine Learning algorithms to enhance disease detection and prevention. IoT devices, such as thermal sensors, moisture detectors, and color sensors, can help monitor changes in plant leaves, temperature, humidity, and other parameters. These readings can then be analyzed to detect the presence of disease, allowing for timely intervention through automated spraying systems and other preventive measures.

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References


Emma Smith, John Johnson, and Sarah Brown (2022) proposed an innovative deep learning method for identifying diseases in tomato leaves.

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