Internet of Thing (IoT) is a groundbreaking technology that has been introduced in the field of agriculture to improve the quality and quantity of food production. As agriculture plays a vital role in feeding most of the world's population, the increasing demand for food has led to a rise in food grain production. The identification of plant diseases is a critical task for farmers and agronomists as it enables them to take proactive measures to prevent the spread of diseases, protect crops, and maximize yields. Traditional methods of plant disease detection involve visual inspections by experts, which can be time-consuming and often subject to human error. However, with technological advancements, IoT and Machine Learning (ML) has emerged as promising solution for automating and improving plant disease identification. This paper explores the challenges and solutions for identifying plant diseases using IoT and ML. The challenges discussed include data collection, quality, scalability, and interpretability. The proposed solutions include using sensor networks, data pre-processing techniques, transfer learning, and explainable AI.
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