Autonomous Mobile Robot for Apple Plant Disease Detection based on CNN and Multi-Spectral Vision System
Pavel Karpyshev, Valery Ilin, Ivan Kalinov, Alexander Petrovsky, Dzmitry Tsetserukou
Abstract
This paper presents an autonomous system for apple orchard inspection and early stage disease detection. Various sensors including hyperspectral, multispectral and visible range scanners are used for disease detection. For localization and obstacle detection 2D LiDARs and RTK GNSS receivers are used. The proposed system allows to minimize the use of pesticides and increase harvests. The detection approach is based on the use of neural networks for both plant segmentation and disease detection.
Topics & Concepts
Computer scienceMultispectral imageHyperspectral imagingArtificial intelligenceComputer visionMachine visionSegmentationMobile robotImage segmentationObstacleConvolutional neural networkPlant diseaseObject detectionRemote sensingRobotGeographyArchaeologyBiologyBiotechnologySmart Agriculture and AISpectroscopy and Chemometric AnalysesRemote Sensing in Agriculture