Varietal Seed Classification and Seed Germination Prediction System

Seed classification and germination prediction are crucial aspects of modern agriculture and ecosystem restoration. Seeds are vital for agriculture and biodiversity conservation, and their accurate classification and germination prediction are crucial for sustainable farming practices. This literature survey paper presents a detailed summary of various machine learning techniques employed for performing seed classification and germination prediction. The paper reviews the latest research, methodologies, and datasets employed in performing seed classification and germination prediction tasks. It also highlights the challenges and opportunities in this domain, including the integration of advanced deep learning models, multispectral imaging, and sensor technologies. The objective of our research is to investigate the impact of environmental factors on the germination potential of diverse seed types, classify seeds based on their physiological characteristics, and develop predictive models for estimating germination timing under varying conditions. By synthesizing existing knowledge, this review aims to establish a basis for future research and advancements in seed-related applications of machine learning.