【Objective】　Develop　a　method　to　improve　the　potato　(Solanum　tuberosum)　leaf　area　index　(LAI)　estimation　accuracy　using　the　UAV　multiple　spectral　wavebands　and　texture　information.　【Method】　The　DJI　P4M　drone　was　used　to　collect　multispectral　images　of　the　southern　winter　potato　at　seedling　period,　budding　period　and　tuber　swelling　period　from　February　to　April　2021.　LAI　data　were　measured　by　LAI-2000　canopy　analyzer.　The　spectral　and　texture　characteristics　of　images　were　extracted.　The　correlations　between　vegetation　index,　texture　characteristics　and　LAI　were　analyzed.　The　selected　characteristic　variables　were　analyzed　based　on　subset　of　adjusted　R2adj.　The　principal　component　analysis　was　used　to　fuse　spectrum　and　texture　features,　and　the　principal　component　analysis-multiple　linear　regression　(PCA-MLR)　model　was　used　to　estimate　potato　LAI.　【Result】　From　the　seedling　period　to　the　tuber　swelling　period,　the　PCA-MLR　estimation　model　was　better　than　texture　multiple　linear　regression　(T-MLR)　and　vegetation　index　multiple　linear　regression　(VI-MLR)　model,　with　R2　of　0.73,　0.59　and　0.66　respectively.　【Conclusion】　This　study　proposed　a　method　of　PCA-MLR　to　estimate　the　potato　LAI　and　improve　the　levels　of　the　potato　growth　monitoring　and　field　management.　©　2023　Editorial　Department,　Journal　of　South　Agricultural　University.　All　rights　reserved.