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Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.

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Changes of soil fertility under different stands was studied by investigating pH value; organic matter; whole amount of N, P, K; available N, P, K and CEC (cation exchange capacity) in 0-30cm depth of soil. Three main types of soils in guangxi was selected which includes mountain yellow soil, brown calcareous soil and lateritic red soil The results showed that stand type affected soil fertility status. For mountain yellow soil, soil organic matter content under Pine forest and adult birch were 2.55 and 3.16 times to natural forest, while the soil available nutrients of newly planted birch was significantly higher than natural forests. For brown calcareous soil, the organic matter, total nitrogen, total phosphorus, http: / / www.ecologica.cn available of nitrogen, phosphorus, potassium and CEC under Zenia forest were the highest, and the pH value under loquat forest was significantly lower than the other three kinds of forest. For lateritic red soil, the available nitrogen under 2nd generation of fast鄄growing eucalyptus forests was significantly lower than natural pine broadleaf forest, but the organic matter, total nitrogen, total potassium, available potassium were slightly higher than the natural forest. The comprehensive evaluation showed soil fertility changed with stand type by the following sequence: natural forest> pine> Southwest birch for mountain yellow soil; Zenia forest = bamboo forest >loquat forest抑Leucaena forest for brown calcareous soil; and natural pine broadleaf forest抑 the 2nd generation of fast鄄growing eucalyptus forests for lateritic red soil.

IntroductionSugarcane is the main industrial crop for sugar production; its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping because it can rapidly predict crop vigor. This paper mainly studied the potential of multispectral images obtained by low-altitude UAV systems in predicting canopy nitrogen (N) content and irrigation level for sugarcane.MethodsAn experiment was carried out on sugarcane fields with three irrigation levels and five nitrogen levels. A multispectral image at a height of 40 m was acquired during the elongation stage, and the canopy nitrogen content was determined as the ground truth. N prediction models, including partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) models, were established based on different variables. A support vector machine (SVM) model was used to recognize the irrigation level.ResultsThe PLS model based on band reflectance and five vegetation indices had better accuracy (R=0.7693, root mean square error (RMSE)=0.1109) than the BPNN and ELM models. Some spectral information from the multispectral image had obviously different features among the different irrigation levels, and the SVM algorithm was used for irrigation level classification. The classification accuracy reached 77.8%.ConclusionLow-altitude multispectral images could provide effective information for N prediction and water irrigation level recognition.