Seed Quality Prediction and Selection Using ML, Image Processing, and IoT Data for Crop Performance

Résumé fourni par la source

Seed quality plays a decisive role in determining crop establishment, growth uniformity, and overall agricultural productivity. Conventional inspection practices, although widely adopted, remain constrained by subjective interpretation, low throughput, and limited capability to assess dynamic field conditions. The rapid advancement of smart agriculture has enabled the integration of machine learning, image processing, and Internet of Things (IoT) technologies to create automated, scalable, and data-driven systems for seed evaluation. This chapter presents a comprehensive framework that leverages high-resolution seed imagery, environmental sensor data, and predictive modeling to support accurate assessment of seed vigor, germination potential, and biological integrity. Deep learning techniques enable automated extraction of phenotypic and morphological characteristics, while IoT sensing ensures continuous monitoring of storage and cultivation environments. Multi-source data fusion and visualization dashboards transform analytical outputs into actionable decision support for farmers and seed processors, facilitating real-time grading, early defect detection, and improved resource allocation. The chapter highlights key methodologies, challenges, validation strategies, and practical implications of deploying intelligent seed quality assessment systems in precision agriculture. The insights provided contribute to the development of robust and scalable digital platforms capable of enhancing productivity, reducing losses, and strengthening data-centric decision-making across the seed supply chain.

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