Performance of the SAFER model in estimating peanut maturation - de Almeida, Samira Luns Hatum [UNESP]; Souza, Jarlyson Brunno Costa [UNESP]; Pilon, Cristiane; Teixeira, Antônio Heriberto de Castro; dos Santos, Adão Felipe; Sysskind, Morgan Nicole; Vellidis, George; da Silva, Rouverson Pereira [UNESP] (Elsevier, 2023)
The most widespread method for obtaining Peanut Maturity Index (PMI), the Hull-Scrape, is time-consuming and highly subjective, which makes its application on a large scale difficult and does not represent the variability of the production area. Seeking more accurate PMI estimates, this research uses a combination of weather and spectral data. Therefore, this study aimed to evaluate the performance of the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model to calculate evapotranspiration and estimate PMI, indicating the optimal timing for crop digging. The experiment was conducted in three commercial peanut fields (A, B, and C) in Georgia, USA, in the 2020 and 2021 growing seasons. Pods were collected on different dates and classified according to maturity using the Hull-Scrape method. Weather data and PlanetScope images were used to calculate actual evapotranspiration from the SAFER model, which was correlated with the PMI collected in situ and used to generate linear regression models. Maturity in Fields A and B showed a stronger correlation with evapotranspiration estimated by SAFER (0.757 and 0.796, respectively), which led to the development of a model using data from these two fields. This model presented a relative error of 13.16% and proved to be the most suitable for estimating peanut maturity by integrating different field conditions. The SAFER model proved to be promising for estimating PMI, as it reduces the subjectivity of the traditional method by eliminating the need for a person to identify the color of pod mesocarp. Additionally, the model does not require images from the given day PMI is estimated, allowing for the estimation even in regions highly affected by the presence of clouds and shadows.
Keywords: Agrometeorology; Arachis hypogaea L; Digital agriculture; Remote sensing; Spectral data