Seed Quality Testing using Deep Learning

Abstract: Seed testing has been developed to aid agriculture to avoid some of the hazards of crop production by furnishing the needed information about different quality attributes viz., purity, moisture, germination, vigour and health. It is very inconvenient to filter out every damaged seed and foreign elements by winnowing in industries and commercial farming. This issue can be minimized if the seeds are filtered in clusters. We have an approach to enhance the efficiency in seed cultivation and seed packaging processes. We created a high-quality dataset which includes fine maize seeds, damaged maize seeds, and foreign elements. By using the Deep Learning technique, the system categorizes an input image as Excellent, Good, Average, Bad and Worst quality seed cluster. The Excellent and Good clusters (sometimes Average) can be cultivated or packaged, and the Bad and Worst clusters can be rejected. We also have recommended the use of object detection to detect and filter out damaged seeds and foreign elements from good quality seed cluster

Keywords: deep learning, seeds, seed cultivation, seed classification