Spectral Diagnostic Model for Agricultural Robot System Based on Binary Wavelet Algorithm.

Abstract

The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R 2 , MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.

Plant phenotyping relevance

スペクトル特徴量と二値ウェーブレット分解を用いて作物の栄養元素含量を推定する診断モデルを構築・評価しており、植物状態の非破壊センシング手法が中心である。

Code and data availability

The paper describes hyperspectral measurements of sweet corn leaves and wavelet/PLS/neural-network diagnostic models, but provides no public dataset, image, code, or model deposit. The Data Availability Statement says 'Not applicable.', and no author URL or repository for assets is given.

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