In　the　study　of　3D　plant　phenotyping,　with　the　efficiency　of　plant　phenotype　improving,　3D　reconstruction　technology　has　become　a　novel　direction　to　obtain　blade　morphology　at　present.　However,　the　traditional　method　of　reconstructing　3D　model　such　as　laser　scanner,　structured　light　image　and　binocular　stereo　vision　system　is　expensive　and　complicated.　A　new　method　extracting　plant　leaf　area　was　proposed　based　on　point　cloud　obtained　by　using　structure　from　motion.　Smart　phone　was　used　to　get　images　of　plants.　Based　on　these　images,　the　three-dimensional　point　cloud　of　plants　was　reconstructed　by　using　structure　from　motion　algorithm.　In　order　to　restore　the　surface　shape　of　the　leaf,　firstly,　the　noise　of　the　leaf　point　cloud　was　removed　by　using　threshold　segmentation　algorithm　based　on　HSV　color　space.　Secondly,　the　three-dimensional　coordinate　matrix　of　point　cloud　was　classified　by　using　K-means　clustering　algorithm　to　segment　single　leaf　point　cloud　by　classifying.　And　then,　the　surface　mesh　model　of　the　leaf　was　reconstructed　by　using　the　ball　pivoting　algorithm.　At　last,　the　leaf　area　was　obtained　by　calculating　the　mesh　area.　To　evaluate　the　proposed　method,　it　was　compared　with　the　conventional　leaf　area　measurement　method.　The　average　error　of　the　calculation　result　of　the　proposed　method　was　4.67%　compared　with　the　measured　value　by　using　the　scanning　method,　and　the　average　error　was　6.05%　compared　with　the　measured　value　by　using　the　leaf-shape　paper　weighing　method.　In　addition,　the　method　of　calculating　leaf　area　by　extracting　contour　by　Canny　edge　detection　algorithm　was　compared　with　the　proposed　method.　The　results　showed　that　the　proposed　method　required　low　cost　and　high　precision,　and　met　the　requirements　of　non-destructive　and　accurate　determination　of　plant　leaf　area.　©　2019,　Chinese　Society　of　Agricultural　Machinery.　All　right　reserved.