Fuzzy-based Bidirectional Long Short Term Memory (f-BiLSTM) based Interpretable Framework for Soil Nutrient Analysis in Precision Agriculture

The escalating global demand for food and water, driven by a rapidly growing population, has intensified interest in precision agriculture. To meet this rising demand, farmers require reliable access to both water resources and arable land. However, the scarcity of these critical resources necessitates a fundamental shift in agricultural practices to enable more efficient utilization. Precision irrigation has emerged as a vital approach to achieving sustainable agriculture, empowering farmers to maximize crop yields while minimizing the use of essential resources. The integration of computer vision algorithms has led to the development of advanced irrigation models that emphasize water conservation. Nevertheless, the limited adaptability of these models hampers their effectiveness in regions with highly variable and unpredictable climatic conditions. In response to this challenge, this study presents a novel intelligent irrigation framework—Fuzzy-based Bidirectional Long Short-Term Memory (f-BiLSTM). This system harnesses the power of deep learning and Internet of Things (IoT) technologies to enable intelligent and efficient water management tailored for modern agricultural applications. A robust feedback mechanism is incorporated to maintain consistent performance across diverse weather conditions and geographical regions, at any given time. Experimental results clearly demonstrate that the f-BiLSTM model significantly outperforms current state-of-the-art irrigation systems in terms of water-use efficiency, establishing it as a highly effective solution for next-generation sustainable farming.