Transfer Learning For Precision Agriculture Improving Identification Of Plant Specie Diseases And Weeds

Abstract

Agriculture plays a critical role in meeting the demand for food in a world with a rapidly growing population. With the global population projected to reach 9.7 billion by 2050, the importance of agriculture cannot be overstated. It serves as the backbone of Indian heritage, defining the way of livelihood in most rural areas, where around 70% of the population depends on agriculture. Plants are vital for agriculture, serving as the primary source of food and ensuring global food security. However, managing plant life presents unique challenges, including plant leaf identification, plant disease classification, plant leaf disease detection, and weed detection. These challenges can significantly impact the quality of crops and overall productivity. Accurate identification of plant species, early detection of diseases, and timely management of weeds are crucial for maximizing crop health and productivity. Traditional plant management methods rely on manual labor and outdated techniques, which are inefficient and limited. To address these challenges, innovative approaches integrating advanced technologies and data-driven decision-making, particularly precision agriculture using deep learning, have emerged. Deep learning algorithms analyze large agricultural datasets, offering valuable insights to improve plant health and agricultural productivity. This thesis makes significant contributions to the field of plant analysis and agriculture, specifically in the areas of plant leaf classification, leaf disease classification, leaf disease detection, and weed detection. newline newline