The Nitrogen Quantitative Model Based on Hyperspectral Image of Tomato Leaf

Based on hyperspectral image which integrates spectrum and image information, the tomato leaf nitrogen model is built. Tomato samples were soillessly cultured with different nitrogen content. The hyperspectral images of tomato leaves are gathered at different stages of tomato. Kjeldahl method determines nitrogen content of the corresponding tomato leaves. According to the hyperspectral information of leaf, the characteristic wave bands of tomato nitrogen are obtained in the method of stepwise regression. The images in the characteristic bands are processed and analyzed, and image features which have high correlation with nitrogen contents of tomato leaves are picked up. The PLS algorithm is used in three growth periods to build tomato nitrogen prediction model, The result of the predicted test shows that the forecast average relative error of PLS model may achieve 10%, and can satisfy the forecast request.