Smart Irrigation in Banana Cultivation: A Comprehensive Review of IoT, Machine Learning and Deep Learning Applications

Abstract

Traditional irrigation practices in banana cultivation often result in inefficient water use, reduced productivity and higher environmental costs. With increasing challenges posed by climate variability and resource scarcity, smart irrigation technologies have emerged as a sustainable solution. This review article synthesises current research on the integration of Internet of Things (IoT), Machine Learning (ML) and Deep Learning (DL) in banana irrigation management. IoT sensors, including soil moisture, temperature, humidity and flow meters, enable real-time data collection and precise water delivery systems. ML algorithms such as regression models, random forests and Support Vector Machines (SVMs) support predictive irrigation scheduling and water requirement forecasting. DL techniques, particularly Convolutional Neural Networks (CNNs), are increasingly applied in image-based monitoring for detecting water stress and disease, integrated with drones and satellite imagery. IoT–ML–DL frameworks, supported by cloud computing and mobile applications, that create automated, data-driven irrigation architectures. Reported benefits include improved water-use efficiency, enhanced banana yield and fruit quality, reduced input costs, environmental conservation and labour savings. Nonetheless, challenges remain in cost, scalability, connectivity in remote regions, data quality and farmer training. Future research and development should focus on affordable sensor networks, AI-driven predictive tools and capacity building to ensure widespread adoption.