Remote Sensing and AI-Driven Sustainable Cotton Farming for a Resilient Future

Abstract. The remote sensing (RS) and geographic information system (GIS) technologies combined with artificial intelligence (AI) enable more efficient and sustainable agricultural ecosystems. In recent years, the use of the machine learning and the deep learning models trained over the geospatial data have emerged as a pivotal catalyst for sustainable and smart agriculture initiatives. The unmanned aerial vehicle (UAVs; drones) has become a transformative force in the context of crop health monitoring, disease detection and yield predictions combined with supervised and unsupervised machine learning that helps to revolutionize the motoring, deep analysis and timely decision making.
This study presents the integration of remote sensing technologies (e.g., UAVs, drones) combined with data-driven artificial technologies to help the farmers in precision agriculture for cotton framing for plant health monitoring against pest infestation, micro irrigation and crop yield prediction. A UAV-based dataset for cotton crops is prepared from a region in Pakistan. The prepared dataset is evaluated through multiple experiments using classical supervised machine learning algorithms for classification: Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). These classification algorithms helped to classify the cotton crop health, healthy or unhealthy. The experimental results indicate that the RF algorithm outperforms the other applied machine learning methods in terms of its accuracy and precision.