IoT-Driven Model for Early Pathogen Detection in Crops using Hyperspectral Imaging, Soil Sensors and Machine Learning

Abstract: The increased need for scalable and real-time solutions has initiated the integration of farming with technologies like IoT, hyperspectral imaging, and machine learning. This paper tries to envisage a new theoretical framework supported by the multichannel data developed from the monitoring through camera-based visual observations, hyperspectral image spectroscopic data, and sensor-based soil health data. The model proposed makes use of data fusion techniques and machine learning algorithms to integrate and analyze diverse data streams to provide insights that surpass single-sensor system diagnostic accuracy. This model offers a proactive approach to the management of diseases by addressing some drawbacks of old methods, such as delayed detection and reliance on heavy resources. It improves diagnosis accuracy, reduces detection time by proposing a model that fuses the visual, spectral, and soil data, and provides real-time actionable insights to farmers via a mobile application. Its adaptability across crops and environmental conditions also points to its wide applicability, more so in precision agriculture. On top of this, the alert system works in real-time, hence interventions on time and reducing crop loss, as well as sustainable farming practices through optimized resource usage. The current paper underlines the theoretical basis of the model, but it also presents ways of future validation through pilot studies and field trials. The proposed model can achieve transformative impacts in agricultural productivity, reduced environmental impact, and global food security by leveraging the combined strengths of IoT with advanced imaging technologies.