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As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900–1000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status.

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BACKGROUNDUnmanned Aerial Spraying System (UASS) has emerged as an advanced, precise, and efficient tool for pesticide application in numerous nations in recent years. Despite this, there is a noticeable gap in research advocating viable, quantifiable methodologies for application parameter optimization. This investigation was primarily oriented toward identifying optimal UASS application parameters. It did so by exploring the effects of varying spray volumes and flight parameters on spray performance in a comprehensive manner, and by assessing the biological potency of aerial insecticide application against Rice Planthopper (RPH) using the optimal parameters, aided by two types of nozzles in rice field settings.RESULTSIncreased spray volume increased the spray deposition. Working height impacted the distribution of spray deposition, with a higher working height leading to superior distribution uniformity. Both spray volume and working height were observed to influence spray deposition and its percentage in tandem. Upon factor analysis, the optimal parameters determined for rice at the heading stage were an application volume of 15.0 L·ha−1, a working height of 2.0 m, and a driving speed of 5.0 m·s−1. Under these parameters, the air‐induction twin flat fan nozzle IDKT120‐015 demonstrated approximately 5% higher spray deposition than the flat fan nozzle SX11001VS, albeit with inferior distribution uniformity. Both nozzle types achieved over 93.0% control efficacy against RPH using triflumezopyrim, persisting for up to 40 days post‐treatment.CONCLUSIONThis study furnishes invaluable insights and data for controlling rice planthopper via UASS pesticide application, contributing to the progress of modern intensive and sustainable agriculture. © 2023 Society of Chemical Industry.