Automated In Situ Seed Variety Identification via Deep Learning: A Case Study in Chickpea.

ABSTRACT: On-time seed variety recognition is critical to limit qualitative and quantitative yield loss and asynchronous crop production. The conventional method is a subjective and error-prone process, since it relies on human experts and usually requires accredited seed material. This paper presents a convolutional neural network (CNN) framework for automatic identification of chickpea varieties by using seed images in the visible spectrum (400-700 nm). Two low-cost devices were employed for image acquisition. Lighting and imaging (background, focus, angle, and camera-to-sample distance) conditions were variable. The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of the

SUBMITTER: Taheri-Garavand A 

PROVIDER: S-EPMC8309301 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature