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This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

This paper proposes an actuator fault detection and isolation strategy based on a bank of unknown input observers with finite frequency specifications. In order to deal with actuator fault diagnosis problem, a bank of H−/H∞ unknown input observers are designed to generate residuals, which are insensitive to the corresponding faults but sensitive to the other actuators faults, and meanwhile robust against the unknown disturbances. In this paper, the actuator faults and unknown disturbances are considered to belong to finite frequency domains, and two finite frequency performance indices are used to measure the fault sensitivity and the disturbance robustness of the residuals. Furthermore, some parameters for extra design of freedom are introduced in the H−/H∞ unknown input observers design. Based on the generalised Kalman‐Yakubovich‐Popov (GKYP) lemma, the design conditions of the H−/H∞ unknown input observer are derived and formulated as linear matrix inequalities (LMIs). Finally, a VTOL aircraft model is used to demonstrate the performance of the proposed fault diagnosis scheme.

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