A comprehensive machine learning and deep learning frameworks for tree species identification from remote sensing images

Abstract

Agriculture monitoring is crucial for ensuring food security and newlinesustainability in the face of a growing global population. Monitoring such aspects newline-as soil moisture, nutrients, weather conditions, pests, and diseases helps optimize newlineresource allocation and minimize environmental impact. By utilizing advanced newlinetechnologies and data-driven approaches, it enables farmers to efficiently manage newlinevarious aspects of agricultural systems and empowers farmers to take timely newlineactions to enhance crop productivity, irrigation, fertilization, and pest control newlinestrategies. Agriculture monitoring contributes to resilient and efficient agricultural newlinesystems by providing valuable insights and enabling the adoption of sustainable newlinepractices. This thesis focuses on the impact of agriculture monitoring, particularly newlinein the context of tree species identification. Tree species identification is crucial for biodiversity conservation, forestry, urban planning, and ecological research. Accurate identification of trees enhances our understanding of tree ecology, distribution patterns, and potential uses. It aids in assessing species abundance, diversity, and distribution, facilitating effective conservation strategies, and protecting endangered species. newlineAdditionally, it helps evaluate the contributions of different species to ecosystem newlineservices like carbon sequestration and soil conservation, supporting informed land newlinemanagement decisions. However, conventional methods for identifying tree newlinespecies are time-consuming and require expertise, posing challenges for newlinenon-specialists. Seasonal variations, specific characteristics, incomplete datasets, newlineand cryptic species further complicate the identification process.- newline newline