Advanced techniques for seed quality assessment and germination monitoring

This comprehensive study examines the pivotal role of technology in seed quality inspection and computer-aided seed germination monitoring. Focusing on cutting-edge automated methods, the review explores how image processing and machine learning techniques are employed for seed quality assessment. It provides a comprehensive overview of the methodologies, image modalities, evaluation approaches, and metrics employed in these advancements, drawing from recent literature sources. The study underscores the importance of real-time monitoring and identifies the requirements for developing automated seed testing systems that minimize human intervention while maximizing productivity in the agricultural sector. By synthesizing supporting literature, the review offers valuable guidance and future directions for researchers seeking to enhance seed quality inspection and implement computer-aided monitoring systems, ultimately improving accuracy and productivity in seed germination processes.