Show simple item record

dc.contributor.authorKirongo, Amos
dc.date.accessioned2019-01-16T14:10:11Z
dc.date.accessioned2020-02-06T14:20:28Z
dc.date.available2019-01-16T14:10:11Z
dc.date.available2020-02-06T14:20:28Z
dc.date.issued2016
dc.identifier.citationKirongo, C. A., & Meru, K. (2016). A Review of Image Processing Software Techniques for Early Detection of Plant Drought Stress, International Journal of Computer Applications Technology and Research, 5(6), 376 - 379.en_US
dc.identifier.issn2319 – 8656
dc.identifier.urihttp://repository.must.ac.ke/handle/123456789/1014
dc.description.abstractWater stress is one of the most important growth-limiting factors in crop production around the world, water in plants is required to permit vital processes such as nutrient uptake, photosynthesis, and respiration. Drought stress in plants causes major production losses in the agricultural industry worldwide. There is no sensor commercially available for real-time assessment of health conditions in beans. Currently, there are several methods to evaluate the effect of water stress on plants and commonly practiced method over the years for stress detection is to use information provided by remote sensing. Studies exist which determined the effect of water stress in plants grown under the different watering regime, while other studies explore the performance of the artificial neural network techniques to estimate plant yield using spectral vegetation indices. This review recognizes the need for developing a rapid cost-effective, and reliable health monitoring sensor that would facilitate advancements in agriculture.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Computer Applications Technology and Researchen_US
dc.subjectImage Processingen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectAlgorithmen_US
dc.titleA Review of Image Processing Software Techniques for Early Detection of Plant Drought Stressen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record