Research Article
Towards a plant pathologies detection solution
@INPROCEEDINGS{10.4108/eai.11-11-2021.2317975, author={Abou SANOU and Jean Serge Dimitri OUATTARA and Didier BASSOLE and Abdoulaye SERE and Yaya TRAORE}, title={Towards a plant pathologies detection solution}, proceedings={Proceedings of the 4th edition of the Computer Science Research Days, JRI 2021, 11-13 November 2021, Bobo-Dioulasso, Burkina Faso}, publisher={EAI}, proceedings_a={JRI}, year={2022}, month={5}, keywords={plant pathologies computer vision efficientnet resnet deep learning}, doi={10.4108/eai.11-11-2021.2317975} }- Abou SANOU
Jean Serge Dimitri OUATTARA
Didier BASSOLE
Abdoulaye SERE
Yaya TRAORE
Year: 2022
Towards a plant pathologies detection solution
JRI
EAI
DOI: 10.4108/eai.11-11-2021.2317975
Abstract
Agriculture is very important in Africa. But farmers are dealing with many plants pathologies that keep agriculture from developing and boosting the economy. Current pathologies diagnosis based on human screening is time consuming and expensive while computer visionbased models are efficiently promising.That is why we address the problem of identification of the pathologies affecting agricultural crops with computer vision tools. Though the high variability of symptoms due to the age of infected tissues, genetic differences, and light conditions in trees reduces the precision of detection, we propose a method to achieve our goal.
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