Wheat Seed Quality Detection For Enhanced Agriculture using YOLOv8 and IOT

Seed quality is a crucial determinant of agricultural productivity and directly impacts crop yield, food security, and resource efficiency. This paper proposes the development of anIot enabled device for real-time wheat seed quality detection. With the use of real-time camera technology and deep learning algorithms like YOLO, it offers quick, non-destructive analysis of important seed quality metrics. In order to ensure the accuracy and dependability of the seed quality detection, the solution focusses on the system architecture, camera integration, and YOLO. In addition to improving seed quality control, IoT and deep learning integrationoffers a framework for agricultural process optimization that raises crop yields, decreases resource waste, and increases global food security initiatives and decision making. Following validation, the following results were obtained: good seed accuracy of $82.59 \%$, bad seed accuracy of $87.26 \%$, and impurity accuracy of $\mathbf{9 3. 1 0 \%}$. The false positive rate for good seed was lower at $12.14 \%$, bad seed at $4.86 \%$, and impurity FPR at $6.79 \%$.