Tarım ve Orman Yönetiminde GeoAI Yaklaşımı: Uzaktan Algılama İle Yapay Zekâ Entegrasyonu

Monitoring agricultural and forestry activities holds critical importance for achieving sustainable development goals. The rapidly growing global population, coupled with climate change and its associated extreme weather events, land degradation, and declining water resources, requires more effective management and monitoring of natural resources. At this point, remote sensing, artificial intelligence (AI), and their synergistic integration, known as Geospatial Artificial Intelligence (GeoAI), offer innovative solutions for agriculture and forestry applications. Currently, satellite constellations such as Sentinel, Landsat, and PlanetScope, alongside unmanned aerial vehicles (UAVs) and hyperspectral sensors, generate a vast amount of high- resolution data related to vegetation health, water stress, soil moisture, and carbon stocks. A recent advancement is the ability to rapidly and efficiently process these massive datasets through machine learning and deep learning methods (e.g., Convolutional Neural Networks and Recurrent Neural Networks). As a result, it has become possible to conduct forest inventories at the individual tree level, predict crop yields, enable the early detection of pest infestations and plant diseases, and develop models for fire and drought risk. The strength of GeoAI resides not only in its capacity for observation but also in its predictive and optimization capabilities. In precision agriculture, AI-driven decision support systems enhance resource efficiency through variable-rate fertilization and irrigation. In forestry, the integration of high-resolution satellite and LiDAR data with deep learning provides new opportunities for estimating biomass and carbon sequestration. Furthermore, object-based image analysis and time-series assessments have emerged as effective approaches for monitoring land cover changes. This integration simultaneously enhances agricultural productivity and supports environmental sustainability. This study examines the current role of remote sensing and AI in agricultural and forestry applications, highlighting key opportunities and challenges, and outlining future research directions.