Crop　colony　is　the　organization　system　which　performs　photosynthesis　and　dry　matter　production　function.　Its　morphological　structure　has　important　influence　on　light　interception　ability,　canopy　photosynthetic　efficiency　and　crop　yield.　The　morphological　characteristics　of　crop　colony　have　always　been　the　most　basic　way　for　people　to　recognize,　analyze　and　evaluate　crops.　Therefore,　it　is　of　great　practical　significance　to　rapidly　and　accurately　model　and　analyze　the　morphology　of　crop　colony　in　a　digital　and　visual　way.　Morphological　data　acquisition　of　maize　colony　is　labor-intensive　and　time-consuming,　and　thus　a　t-distribution　based　three-dimensional　(3D)　maize　colony　modeling　method　was　proposed　using　a　few　measured　data.　The　method　constructs　t-distribution　function　of　primary　plant　morphological　parameters　using　measured　data　and　generates　random　plant　morphological　parameters　under　the　constraint.　The　main　plant　morphological　parameters　include　plant　and　phytomer　scale.　Here　plant　scale　parameters　include　plant　height,　total　leaf　number,　and　first　leaf　index,　and　phytomer　scale　parameters　include　leaf　growth　height,　leaf　insertion　angle,　leaf　length,　leaf　width,　and　leaf　azimuthal　angle.　Particularly,　leaf　azimuthal　angles　are　generated　using　the　deviations　between　the　plant　azimuthal　plane　and　leaf　azimuths.　High　quality　geometric　models　in　3D　template　resource　database　of　maize　organs　are　selected　by　constructing　a　similarity　assess　function　of　plant　morphological　parameters.　Leaf　length,　leaf　insertion　angle,　leaf　index,　and　plant　cultivar　are　the　control　parameters　in　the　function.　Then　geometric　models　of　individual　plants　in　target　colony　are　generated.　Interactive　design　or　field　image　extraction　method　is　used　to　allocate　the　growth　positions　and　plant　azimuthal　planes　of　each　plant　in　the　colony.　Maize　colony　is　generated　by　moving　and　rotating　operations　of　each　plant　according　to　the　designed　or　extracted　growth　positions　and　plant　azimuthal　planes.　Leaf　area　index　(LAI)　is　used　to　validate　the　generated　maize　colony　model.　Three　in-situ　field　measurement　experiments　in　Qitai　County　of　Xinjiang　using　3D　digitizer　were　carried　out　to　reconstruct　geometric　models　of　maize　colony,　and　the　cultivar　was　Xianyu　335　and　the　planting　densities　were　105,　135,　and　165　thousand　plants/hm2,　as　true　values　for　LAI　calculating.　Corresponding　plant　morphological　parameters　of　the　corresponding　colonies　were　measured.　The　maize　colony　modeling　method　based　on　t-distribution　function　was　used　to　construct　3D　models　and　LAI　was　also　calculated　for　the　colonies.　Results　show　that　the　LAI　errors　are　less　than　±2%.　In　addition,　generalized　LAI　of　different　heights　of　plant　colony　is　proposed　to　provide　more　detailed　verification　in　different　height　levels.　The　averaged　RMSE　(root　mean　square　error)　of　Xianyu　335　with　the　density　of　135　thousand　plants/hm2　is　0.023,　and　the　averaged　NRMSE　(normalized　root　mean　square　error)　is　0.425,　which　demonstrate　that　it　has　a　good　consistency　of　spatial　leaf　distribution　between　the　in-situ　measured　field　colony　and　reconstructed　colony　using　t-distribution.　These　results　show　that　the　proposed　maize　colony　modeling　method　could　meet　the　needs　of　plant　functional-structural　analysis.　Compared　with　the　existing　methods,　the　proposed　method　is　more　effective　and　highly　realistic,　and　the　constructed　maize　colonies　are　capable　of　reflecting　the　agronomic　characteristics　of　the　target　colony,　such　as　the　differences　caused　by　intrinsic　cultivar,　environment,　planting,　or　management　factors.　Maize　colony　model　could　be　rapidly　generated　by　simple　modification　of　morphological　input　parameters.　Combined　with　the　light　distribution　simulating　algorithm,　a　large　number　of　maize　colony　models　will　be　designed　for　virtual　experiments.　It　has　great　importance　for　the　research　and　application　of　maize　plant　morphology　optimization,　estimation　of　planting　density,　adaptability　evaluation　of　different　cultivars,　and　cultivation　strategy　decision.　Due　to　the　complexity　of　maize　colony　structure　morphology,　there　are　still　many　subsequent　colony　modeling　issues　that　will　be　addressed　in　future　research,　such　as　adjacent　phytomer　parameters　constraint　model　construction,　plant　collision　detection　and　collision　response,　and　colony　mesh　simplification　and　optimization　for　visual　computing.　©　2018,　Editorial　Department　of　the　Transactions　of　the　Chinese　Society　of　Agricultural　Engineering.　All　right　reserved.