首页|National Research Council (CNR) Researchers Update Understanding of Artificial I ntelligence (GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat)
National Research Council (CNR) Researchers Update Understanding of Artificial I ntelligence (GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news originating from Firenze, Ital y, by NewsRx editors, the research stated, "Capitalizing on the widespread adopt ion of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in th e agricultural domain. This paper introduces GranoScan, a freely available mobil e app accessible on major online platforms, specifically designed for the real-t ime detection and identification of over 80 threats affecting wheat in the Medit erranean region." The news journalists obtained a quote from the research from National Research C ouncil (CNR): "Developed through a co-design methodology involving direct collab oration with Italian farmers, this participatory approach resulted in an app fea turing: (i) a graphical interface optimized for diverse in-field lighting condit ions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward o perational guide, and (v) the ability to specify an area of interest in the phot o for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an e nsembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem diseas e tasks. For weeds in the post-germination phase, the precision values range bet ween 80% and 100%, while 100% is reache d in all the classes for preflowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances , with a mean accuracy of 77% and 95% for leaf disea ses and for spike, stem and root diseases, respectively."
National Research Council (CNR)FirenzeItalyEuropeArtificial IntelligenceEmerging TechnologiesMachine Learnin g
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