SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging

PixISegNet: Pixel Level Iris Segmentation Network using Convolutional Encoder-Decoder with Stacked Hourglass Bottleneck

(2019) Gaurav Jaswal et al.   IET Biometrics

Detection and analysis of wheat spikes using Convolutional Neural Networks

(2018) Md Mehedi Hasan et al.   Plant Methods

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

(2017) Péter Bognár et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization

(2017) Xiong Xiong et al.   Plant Methods

Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery

(2017) Xiuliang Jin et al.   REMOTE SENSING OF ENVIRONMENT

Deep Learning for Image-Based Cassava Disease Detection

(2017) Amanda Ramcharan et al.   Frontiers in Plant Science

Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering

(2017) Pouria Sadeghi-Tehran et al.   Frontiers in Plant Science

Using Deep Learning for Image-Based Plant Disease Detection

(2016) Sharada P. Mohanty et al.   Frontiers in Plant Science

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

(2016) Srdjan Sladojevic et al.   Computational Intelligence and Neuroscience

Unsupervised detection of non-iris occlusions

(2015) Michal Haindl et al.   PATTERN RECOGNITION LETTERS

Slicing the wheat genome

(2014) K. Eversole et al.   SCIENCE

The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance

(2009) Hugo Proenca et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE