| dc.contributor.author | Memeu, Daniel Maitethia | |
| dc.contributor.author | Kirongo, Amos | |
| dc.contributor.author | Boiyo, Richard | |
| dc.contributor.author | Kimuya, Alex M | |
| dc.date.accessioned | 2019-01-16T13:43:22Z | |
| dc.date.accessioned | 2020-02-06T14:20:30Z | |
| dc.date.available | 2019-01-16T13:43:22Z | |
| dc.date.available | 2020-02-06T14:20:30Z | |
| dc.date.issued | 2017 | |
| dc.identifier.issn | 2319 - 1058 | |
| dc.identifier.uri | http://dx.doi.org/10.21172/ijiet.81.001 | |
| dc.identifier.uri | http://repository.must.ac.ke/handle/123456789/1027 | |
| dc.description.abstract | Crop stresses are often detected late and this leads to massive crop yield loss. There exists a need for development of a real time system for monitoring crop growth progress with a view of early detection of diseases and other forms of crop stress. In this work, a simple raspberry pi imaging system is developed and used to capture images of beans subjected to drought stress. The images are used to extract features for classification of the stress. The features are then used to train classifiers for drought stress detection. Detection accuracy of between 90 to 100% has been achieved. | en_US |
| dc.description.sponsorship | Kenya Education Network (KENET), Raspberry Pi mini-grant and Meru University of Science and Technology | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | International Journal of Innovations in Engineering and Technology | en_US |
| dc.subject | Computer vision | en_US |
| dc.subject | Raspberry Pi | en_US |
| dc.subject | Artificial Neural Network | en_US |
| dc.subject | Segmentation | en_US |
| dc.title | Suitable Image Features for Drought Stress Detection in Beans using Raspberry PI Imaging System | en_US |
| dc.type | Article | en_US |
