title: Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery.
abstract: Thermal imaging has been used in the past for remote detection of regions of canopy showing symptoms of stress, including water deficit stress. Stress indices derived from thermal images have been used as an indicator of canopy water status, but these depend on the choice of reference surfaces and environmental conditions and can be confounded by variations in complex canopy structure. Therefore, in this work, instead of using stress indices, information from thermal and visible light imagery was combined along with machine learning techniques to identify regions of canopy showing a response to soil water deficit. Thermal and visible light images of a spinach canopy with different levels of soil moisture were captured. Statistical measurements from these images were extracted and used to classify between canopies growing in well-watered soil or under soil moisture deficit using Support Vector Machines (SVM) and Gaussian Processes Classifier (GPC) and a combination of both the classifiers. The classification results show a high correlation with soil moisture. We demonstrate that regions of a spinach crop responding to soil water deficit can be identified by using machine learning techniques with a high accuracy of 97%. This method could, in principle, be applied to any crop at a range of scales.
authors.affiliation: Department of Computer Science, University of Warwick, Coventry, United Kingdom.
authors.forename: Shan-e-Ahmed
authors.initials: SE
authors.lastname: Raza
authors.affiliation: Centre for Biological Sciences, Life Sciences, University of Southampton, Southampton, United Kingdom.
authors.forename: Hazel K
authors.initials: HK
authors.lastname: Smith
authors.affiliation: Vitacress Salads Ltd., Lower Link Farm, St Mary Bourne, Andover, United Kingdom.
authors.forename: Graham J J
authors.initials: GJ
authors.lastname: Clarkson
authors.affiliation: Centre for Biological Sciences, Life Sciences, University of Southampton, Southampton, United Kingdom.
authors.forename: Gail
authors.initials: G
authors.lastname: Taylor
authors.affiliation: Soil and Agri-Food Institute, School of Applied Sciences, Cranfield University, Bedford, United Kingdom.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Thompson
authors.affiliation: School of Life Sciences, University of Warwick, Wellsbourne, United Kingdom.
authors.forename: John
authors.initials: J
authors.lastname: Clarkson
authors.affiliation: Department of Computer Science, University of Warwick, Coventry, United Kingdom; Department of Computer Science and Engineering, Qatar University, Doha, Qatar.
authors.forename: Nasir M
authors.initials: NM
authors.lastname: Rajpoot
mesh_terms: D057914:Agricultural Irrigation; D001331:Automation; D018556:Crops, Agricultural / Q000502:physiology; D006813:Humidity; D021621:Imaging, Three-Dimensional; D016011:Normal Distribution; D018515:Plant Leaves / Q000502:physiology*; D025341:Principal Component Analysis; D011336:Probability; D012044:Regression Analysis; D012987:Soil / Q000737:chemistry*; D018724:Spinacia oleracea / Q000502:physiology*; D060388:Support Vector Machine; D013696:Temperature
chemical_list: D012987:Soil