Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Horticulture
Classim: A Relational Database Driven Crop Model Interface, Dennis Timlin, David Fleisher, Maura Maura, Kirsten Paff, Wenguang Sun, Sahila Beegum, Sanai Li, Zhuangji Wang, Vangimalla Reddy
Classim: A Relational Database Driven Crop Model Interface, Dennis Timlin, David Fleisher, Maura Maura, Kirsten Paff, Wenguang Sun, Sahila Beegum, Sanai Li, Zhuangji Wang, Vangimalla Reddy
Department of Agronomy and Horticulture: Faculty Publications
Crop models are valuable tools for examining the interactions of cultivar characteristics, environment, and management practices, and how they affect crop growth and development. The difficulty in finding all the data needed to set up a simulation can often deter potential users from utilizing a crop model. Model interfaces are necessary to make these complex tools accessible to end-users who may lack the expertise needed to work with the models directly, but who would benefit from the information generated by the models. As crop models vary in terms of input and output structures, there is no one universally compatible interface, …
A Deep Learning Framework For Processing And Classification Of Hyperspectral Rice Seed Images Grown Under High Day And Night Temperatures, Víctor Díaz-Martínez, Jairo Orozco-Sandoval, Vidya Manian, Balpreet K. Dhatt, Harkamal Walia
A Deep Learning Framework For Processing And Classification Of Hyperspectral Rice Seed Images Grown Under High Day And Night Temperatures, Víctor Díaz-Martínez, Jairo Orozco-Sandoval, Vidya Manian, Balpreet K. Dhatt, Harkamal Walia
Department of Agronomy and Horticulture: Faculty Publications
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature …