Abstract:
Research on the agriculture sector has increased within the past few years. This study
focuses on developing a transfer learning-based approach to improve vegetable seed
recognition and verification, addressing the crucial demand for accurate seed
classification. The agricultural economy of Bangladesh heavily relies on the quality of
seeds, yet manual classification methods are time-consuming and error-prone. There
are several features to classify a seed, such as color, length, texture, size, shape etc. This
research evaluates the classification performance of three transfer learning models
MobileNetV2, Inception V3, and NASNetMobile on a dataset comprising 6,000
preprocessed images, with each of the 12 vegetable seed varieties represented by 500
images. The research focuses on enhancing model performance through adjustments in
image resolution and epoch parameters. Out of all the experiments, MobileNetV2
provides an accuracy rate of 99.50% and a loss of 1.57%, this performance is validated
by the test dataset. These implications enhance agricultural seed classification by
improving efficiency and accuracy.
