Use of Artificial Intelligence and IoT for Seed Quality Testing

Abstract— The quality of seeds is a fundamental factor in ensuring agricultural productivity and food security. Traditional seed quality assessment methods, such as manual inspection and laboratory-based testing, are often time-consuming, labor-intensive and prone to human error. To overcome these limitations, modern agricultural technologies have increasingly integrated Artificial Intelligence (AI) and the Internet of Things (IoT) to enhance seed quality evaluation. AI-driven models, including deep learning and computer vision techniques, have demonstrated high accuracy in detecting seed defects, predicting germination potential and classifying seeds based on various parameters (Kundu et al., 2021). Additionally, IoT-based smart sensors enable real-time monitoring of critical environmental factors such as humidity, temperature and storage conditions, ensuring optimal seed preservation (Kler et al., 2023). The fusion of AI and IoT facilitates automated, high-throughput and non-destructive seed quality testing using hyperspectral imaging, near-infrared spectroscopy and cloud-based analytics. While these advancements offer numerous benefits, challenges such as high implementation costs, data security concerns and the need for technical expertise hinder widespread adoption. This review demonstrate that integrating IoT-driven sensing with advanced AI methods offers a scalable, objective solution for seed certification, with potential extensions to disease detection, phenotypic trait analysis, and adaptive sorting in commercial processing lines.