Demystifying the Data: Leveraging Explainable AI for Real –Time Monitoring and Decision-Making in Vertical Hydroponics Farms

A viable option for the productive and sustainable production of green vegetables is vertical hydroponic farms. However, in these regulated circumstances, making the best decisions and monitoring in real time might be difficult. This study investigates how Explainable Artificial Intelligence (XAI) might be used to overcome these obstacles. We suggest a unique approach that combines multispectral imaging of the plants with sensor data from temperature, humidity, and nutrient levels. For real-time analysis, a machine learning model is then used, which has been trained on this combined dataset as well as previous data on disease outbreaks. Through the use of XAI approaches, the system is able to provide interpretable reasons for its conclusions in addition to identifying anomalies and possible illness signals. This enables farmers to make wise decisions for maximizing environmental conditions and obtain insightful knowledge on the health of their crops and prevent disease outbreaks. The purpose of this study is to show how successful XAI is in vertical hydroponic farming and how it can transform data-driven decision-making, real-time monitoring, and crop health and yield in these regulated settings.