Enhancing Agricultural Management With Internet of Things and Deep Learning
TL;DR
This work proposes an IoT‐enabled smart farming system integrating machine learning, deep learning, and automation for agricultural management that improves productivity, efficiency, and sustainability in resource‐constrained agricultural environments.
Abstract
Modern agriculture faces several critical challenges, including unpredictable weather conditions, inefficient resource utilization, and pest infestations, all of which negatively impact crop productivity and sustainability. Traditional farming practices often lack automation and data‐driven decision‐making, leading to increased operational costs, resource wastage, and reduced yields. Furthermore, many existing smart farming solutions remain fragmented, focus on isolated tasks, require high computational resources, and are not well‐suited for resource‐constrained environments, highlighting the need for a more integrated and cost‐effective approach. To address these challenges, this work proposes an IoT‐enabled smart farming system integrating machine learning, deep learning, and automation for agricultural management. The system automates operations such as plowing, sowing, irrigation, fertilization, pesticide, and weedicide application through an Android‐based interface. A Naive Bayes model is employed for crop recommendation based on environmental parameters, enabling data‐driven decision‐making, achieving an accuracy of 96.5%. The system incorporates real‐time soil moisture and temperature monitoring for automated irrigation control, improving irrigation efficiency, resulting in up to 90% improvement in water‐use efficiency compared to conventional methods. Pest detection is performed using a hybrid approach combining Passive Infrared (PIR) sensing and sound analysis, achieving an accuracy of approximately 96%. Weed detection is performed using an Inception‐based deep learning model, achieving a training accuracy of 99.9% and validation accuracy of 99%. Soil health assessment is performed using an optical transducer‐based approach for estimating NPK levels, providing a low‐cost, real‐time solution. The integrated IoT–AI framework improves productivity, efficiency, and sustainability in resource‐constrained agricultural environments.