Computer Vision

Agriculture plays a vital role in global food security, and maintaining plantation health is essential for maximizing productivity and sustainability. Traditional plantation monitoring methods rely heavily on manual field inspections, which are time-consuming, costly, and often inaccurate due to human limitations. Automated plantation health monitoring has emerged as a promising solution due to the quick development of machine learning and remote sensing technology. This study suggests a machine learning-based system for automatically monitoring plantation health using drone-acquired data and sophisticated image processing techniques. To categorize plantation areas as healthy, stressed, or diseased, the system examines the visual and spectral characteristics of crops. While vegetation indices like NDVI help measure plant vitality, a convolutional neural network (CNN) is used to directly understand complicated patterns from photos. According to experimental results, the suggested method achieves excellent classification accuracy and reliability, which qualifies it for widespread use in agriculture. Precision agriculture techniques are supported, early disease diagnosis is made possible, and labor effort is decreased.