Deep learning and IoT for Monitoring Tomato Plant

Abstract

Agriculture is the primary food source for humans and livestock in the world and the primary source for the economy of many countries. The majority of the country’s population and the world depend on agriculture. Still, at present, farmers are facing difficulty in dealing with the requirements of agriculture. Due to many reasons, including different and extreme weather conditions, the abundance of water quality, etc. This paper applied the Internet of Things and deep learning system to establish a smart farming system to monitor the environmental conditions that affect tomato plants using a mobile phone. Through deep learning networks, trained the dataset taken from PlantVillage and collected from google images to classify tomato diseases, and obtained a test accuracy of 97%, which led to the publication of the model to the mobile application for classification for its high accuracy. Using the IoT, a monitoring system and automatic irrigation were built that were controlled through the mobile remote to monitor the environmental conditions surrounding the plant, such as air temperature and humidity, soil moisture, water quality, and carbon dioxide gas percentage. The designed system has proven its efficiency when tested in terms of disease classification, remote irrigation, and monitoring of the environmental conditions surrounding the plant. And giving alerts when the values of the sensors exceed the minimum or higher values causing damage to the plant. The farmer can take the appropriate action at the right time to prevent any damage to the plant and thus obtain a high-quality product.