The kernel water concentration (KWC) varies more intensely during the physiological dehydration phase, and the dehydration process during this phase has been demonstrated to be closely associated with internal physiological processes in maize kernels. To better illustrate the physiological dehydration process (PDP) of maize kernels, moisture characteristic parameters were consecutively recorded after pollination, and the timing of the occurrence of net water loss was confirmed. Additionally, a tandem mass tag (TMT) based proteomic analysis was performed to identify the significantly differentially expressed proteins during the critical period of the PDP (PDP cp). The results showed that maize varieties presented significant variance in the KWC, kernel water content, dry-down rate (DR) and absolute DR; nevertheless, the KWC was similar (below 65%) when net water loss occurred. In addition, 20-28 days after pollination was indicated to be the PDP cp in this study. Based on proteome analysis, Yuyu 30 and Suyu 41 showed 163 and 132 differentially expressed proteins (DEPs), respectively during the PDP cp and 99 DEPs overlapped between the varieties. During the PDP cp , the desiccation tolerance, antioxidation capability, detoxification and other stress response abilities of maize kernels were significantly increased. The increased defense abilities enabled the maize kernels to establish coordinated, diverse, stable defense mechanisms that accelerated water loss from maize kernels during the PDP cp .

In the field of precision agriculture research, it is very important to monitor crop growth in time so as to effectively conduct field diagnosis and management and accurately predict yield and quality. In this experiment, the relationship between the vegetation index of Zhengdan 958 and Suyu 41 and their yield and quality when reducing N application (25 and 50% N reduction compared to local conventional N application rate) under low, medium and high planting densities (60,000, 75,000 and 90,000 plants·ha−1) during 2018–2020 was investigated using multispectral images obtained from UAV monitoring. The results showed that under different density treatments, the normalized vegetation index (NDVI) and ratio vegetation index (RVI) decreased with the decrease in nitrogen application, while the plant senescence reflectance index (PSRI) increased. Through principal component analysis (PCA) and subordinate function analysis, the comprehensive score of each treatment can reflect the maize yield and total protein content under each treatment. Based on the vegetation index, predictive models of maize yield and protein content were established. The best prediction period for grain yield and protein content were physiological maturity and 35 days after silking (R4), respectively. The R2 of the predictive models are greater than 0.734 and 0.769, respectively. Multi-period and multi-vegetation indexes can better monitor crop growth and help agricultural field management.