This manuscript presents a comprehensive survey on recent computer vision based food
grain classification techniques. It includes state-of-the-art approaches intended for different …

This study focuses on the potential of multi-omics and machine learning approaches in
improving our understanding of the malting processes and cultivation systems in barley. The …

Rice, which has the highest production and consumption rates worldwide, is among the
main nutrients in terms of being economical and nutritious in our country as well. Rice goes …

For the purpose of improving the extraction of tea plant leaf disease saliency map under
complex backgrounds, a new algorithm combining SLIC (Simple Linear Iterative Cluster) …

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 …

Highlights•Deep learning and transfer learning CNNs are compared in barley varietal
classification.•Simplifying the CNN model has positive impact on classification results.•Only …

The demand for identification of cereal products with computer vision based applications
has grown significantly over the last decade due to economic developments and reducing …

Artificial neural networks (ANNs) have many applications in various scientific areas such as
identification, prediction and image processing. This research was done at the Islamic Azad …

Data on physical properties of grains and seeds have significant importance for machinery
and process equipments design. The study was performed to investigate selected …

In archaeobotanical studies, the taxonomic classification of diaspores has usually been
done by simple morphological observation and visual comparison with ex situ collections of …