Agriculture is a key contributor to Pakistan’s GDP, and optimizing fertilization is crucial for enhancing crop yield and ensuring food security. This research presents a real-time, IoT-based soil analysis model that replaces traditional off-site testing, providing instant and site-specific fertilizer recommendations. The system integrates an IoT-enabled device to assess soil nutrient levels and employs a regression algorithm to predict the required NPK quantities. A realistic soil dataset is used to train and validate the model, ensuring accurate predictions. With an 88-92% accuracy rate, the system effectively recommends fertilizers, enabling precision farming and optimizing resource utilization. This reduces reliance on conventional soil testing methods, minimizing fertilizer wastage and improving soil sustainability. The real-time analysis supports data-driven farming decisions, ensuring balanced nutrient application and promoting sustainable agricultural practices. Additionally, this innovation aligns with the Sustainable Development Goals (SDGs) by modernizing agricultural techniques, enhancing food security, and supporting economic growth in farming communities.The IoT-based smart fertilizer recommendation system offers a cost-effective, accurate, and sustainable solution to improve agricultural productivity and promote precision farming.
Irfan et al. (Thu,) studied this question.
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