IOT-ENABLED SMART CROP DISEASE DETECTION SYSTEM USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES

This review paper examined various studies related to IoT-based crop disease detection using sensor and image data. Crop diseases continue to be a major challenge in agriculture, causing significant losses in yield and quality. Traditional disease detection methods are often time-consuming and ineffective, especially for large-scale farming. The study highlights that IoT sensors play an important role in continuously monitoring environmental parameters such as temperature, humidity, soil moisture, and pH, which directly influence crop health. Image processing and machine learning techniques, particularly deep learning models, enable accurate identification of disease symptoms from crop leaf images. The integration of sensor data with image-based analysis improves the reliability and accuracy of disease detection systems. The review also reveals that IoT-based crop disease detection systems support early diagnosis, real-time monitoring, and timely decision-making for farmers. Such systems help reduce crop loss, minimize excessive pesticide usage, and promote smart and sustainable agricultural practices. Overall, the study concludes that IoT-enabled disease detection systems have significant potential to transform traditional agriculture into intelligent farming, thereby improving productivity and ensuring food security.

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