【正文】 Leaves Classification and Leaf Mass Estimation Summary For the first problem, we establish our neural work model to classify leaves of trees by taking eight characteristics of leaf into consideration. The eight characteristics consist of sawtooth number, petiole length, blade length, blade width, blade thickness, leaf area and circular degree. Our results are summarized in a conclusion that we classify leaves into fourteen types including linear, lanceolate, oblanceolate, spatulate, ovat, obovate, elliptic, oblong, deltoid, reniform, orbicular, peltate, perfoliate and connate. Our neural work implement the classification task reliably and correctly. For the second problem, we set up our AHP model to figure out the reasons why leaves have the various shapes and e to a conclusion that gene, auxin, climate and disease are the main reasons which lead to various shapes. For the third problem, we discuss this issue from the perspective of growth evolutionary and hormones, build cells mechanic model to solve this problem and sum up the conclusion that the shapes are inclined to minimize overlapping individual shadows that are cast so as to maximize exposure. The shape is effected by the distribution of leaves within the volume of the tree and its branches. For the fourth problem, we use statistical analysis knowledge to analyse the data among tree profiles, branching structure and leaf shapes, after mathematically analyzing, finally find that leaves shapes have a direct relation with the tree profile and branching structure, For the fifth problem, we formulate our volumetric method for leaf mass estimation and linear regression model for seeking and paring the correlation between the leaf mass and tree height, tree mass and crown volume. We obtain that crown volume has the highest correlation with tree leaf mass. So we make use of the crown volume to estimate the leaf mass. At last ,we write one page summary sheet of our key findings. Key words: neural work, leaf classification, leaf mass estimation, AHP, leaf shape, volumetric method, linear regression model Team 15263 Page 0 of 23 Contents Contents ........................................................................................................................... 0 Ⅰ . Introduction ................................................................................................................. 1 Ⅱ . Some Definitions .......................................................................................................... 1 Ⅲ . General Assumptions .................................................................................................... 1 Ⅳ . Symbols ...................................................................................................................... 2 Ⅴ . Problem analysis .......................................................................................................... 2 Ⅵ . Models ........................................................................................................................ 3 Neural work model to classify tree leaves ............................................................. 3 Neuromime ................................................................................................. 3 Multilayer perceptron work...................................................................... 4 Backpropogation ........................................................................................ 5 . 4 NN?s use to classify leaves ........................................................................... 6 Studying the reasons of the various shapes that leaves have. ........................................ 6 Set up a AHP model to value these base factors ............................................... 6 Paired parison matrix structure ................................................................ 7 Calculation of the weight vector and the consistency test................................... 8 Optimize leaves shape for maximize exposure ........................................................... 9 Explain and answer requirment ...................................................................... 9 Set up a Elastic mechanics model ................................................................... 9 Tree profile and branching structure?s influence on leaf shape. .................................. 10 Analysis about the impact of tree profile to leaf shape..................................... 10 Electric tree branch angle?s impact analysis ................................................... 13 Estimation of the leaf mass ................................................................................... 14 Build up a volumetric model........................................................................ 14 The correlation of leaf mass vs. mean crown radius?s cubic ............................. 15 The correlation between the leaf mass and the height of the tree ....................... 16 The dry leaf mass vs. the volume of the tree .................................................. 17 The relationship between the leaf mass and mean crown radius ........................ 18 Ⅶ . Conclusions ............................................................................................................... 19 Ⅷ . Strengths and Weakness of the Model............................................................................ 19 Ⅸ . Future Work ............................................................................................................... 20 Ⅹ . References ................................................................................................................. 20 Key Findings .........