IoT-Based Framework for Implementation of Leaf Color Chart for Nitrogen Monitoring in Crop

Abstract

Effective nitrogen management is essential in precision agriculture to optimize crop yields and promote environmental sustainability. Conventional techniques for evaluating nitrogen levels, although commonly used, suffer from a lack of accuracy and scalability. This research presents a novel design framework that combines the Leaf Colour Chart (LCC), a well-established agricultural tool for nitrogen evaluation, with modern Internet of Things (IoT) technologies. The proposed system utilizes specialized sensors to gather precise leaf color data, which is subsequently analyzed using cloud-based computer vision techniques to correctly and instantaneously predict nitrogen levels. The framework streamlines data gathering and processing, allowing for accurate nitrogen management. This enables the application of fertilizer to specific areas and minimizes wastage. This article provides an overview of the system architecture, examines the difficulties and resolutions in integrating Internet of Things (IoT) technology in agriculture, and showcases case studies that demonstrate the effectiveness of the framework in practical environments. The incorporation of IoT technology with the Leaf Colour Chart signifies a notable progression in agricultural technology, offering the potential to enhance crop management techniques and support sustainable agriculture endeavors.