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Doctoral Thesis | Computer Science and Information Technology | India | Volume 12 Issue 11, November 2023 | Rating: 6.6 / 10

Seeds in Symphony: Unveiling Varietal Diversity with Hybrid Feature Classification

Ramalinga Reddy | Dr. Suma R

Abstract: In the agricultural realm, the classification of seed varieties plays a pivotal role in enhancing crop management and fostering agricultural sustainability. Traditional methods of seed classification are often time - consuming and labor - intensive. This paper proposes a hybrid feature classification approach to unveil varietal diversity in seeds more efficiently which combines the power of computer vision and machine learning techniques. We first extract a set of visual features from seed images using state - of - the - art computer vision algorithms. These features capture various characteristics such as shape, color, and texture, which can help differentiate different seed varieties. This abstract invites readers to delve into the world of seed varietal classification, where the harmony of hybrid features orchestrates a symphony of insight into the rich diversity of agricultural seeds. "Seeds in Symphony" stands as a testament to the potential of hybrid feature classification in advancing precision agriculture and contributing to the sustainable future of crop cultivation.

Keywords: CNNs, Data Extraction, Machine Learning, Testing, Seeds, Classifiers

Edition: Volume 12 Issue 11, November 2023,

Pages: 1458 - 1465