Conventional irrigation techniques face obstacles pertaining to water loss and scheduling, which hinder the capacity to meet the growing demands for food production while preserving vital water supplies. In order to overcome these obstacles, this study presents an innovative smart irrigation System that improes water application efficiency and scheduling by combining machine learning data analysis, and the Internet of Things (loT). The foundation of this creative method is the collection of historical Environmental data from the West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Federal University of Technology, Akure and Soil profile Dataset form Nigeria National Legacy data and lITA Project sites making it the foundational dataset needed for accurate irrigation management. The smooth functioning of the system is ensured by using Internet of Things (loT) sensors for real-time field data collection. Preprocessing techniques are then used i guarantee the quality and consistency of the data. This carefully selecied dataset provides real-time intelligent irrigation decisions by acting as the training set for a machine learning algorithm. The irrigation process's control mechanisms work in perfect harmony with the model's predictions Additionally, the system makes it easier for agricultural professionals to monitor and modijy irrgation tactics. The system's efectiveness is validated by the experimental findings, which highlight the decision tree model in particular because of its superior balance of accuracy. precision, recall, and Fl score. Through the optimization of agricultural yield and the reduction of water loss, this method enhances productivity and sustainability.
Author(s): Adedeji Samuel Adeleye, Olumide Sunday Adewale, Samuel Adebayo Oluwadare and Akintoba Emmanuel Akinw
