High-resolution satellite image to predict peanut maturity variability in commercial fields

• Published in: Precision agriculture Vol. 22; no. 5; pp. 1464 - 1478
• Main Authors: dos Santos, Adão Felipe, Corrêa, Lígia Negri, Lacerda, Lorena Nunes, Tedesco-Oliveira, Danilo, Pilon, Cristiane, Vellidis, George, da Silva, Rouverson Pereira
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-10-2021; Springer Nature B.V
• Subjects: Agriculture; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Commercial spacecraft; Computer Science; Harvest; High resolution; Human error; Image resolution; Legumes; Life Sciences; Maturity; Nondestructive testing; Peanuts; Physics; Production increases; Remote sensing; Remote Sensing/Photogrammetry; Satellite imagery; Satellites; Soil Science & Conservation; Soil sciences; Statistics for Engineering; Subjectivity; Sugarcane; Variability; Vegetation; PlanetScope images; L; Vegetation indices; Remote sensing; Precision harvest
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-021-09791-1

One of the main problems in the peanut production process is to identify the pod maturity stage. Peanut plants have indeterminate growth, which leads to a high pod maturity variability within the same plant. Moreover, the actual method of determining maturity is destructive and highly subjectivity, which does not represent the overall variability in the field. Hence, the main goal of this study was to verify the possibility to estimate peanut maturity and its in-field variability using an alternative non-destructive method based on orbital remote sensing. High-resolution satellite images (~ 3 m) were obtained from the PlanetScope platform for two commercial peanut fields in São Paulo state, Brazil, during the reproductive stage of the peanut crop (89 to 118 days after sowing—DAS). The fields were divided into 54 plots (30 × 30 m). The maturity was obtained using the Hull Scrape method. All Vegetation Indices (VIs) used showed a high Pearson correlation (p < 0.001) between peanut maturity and the VIs, with values decreasing as maturity increased. Non-Linear Index (NLI) values from 0.561 to 0.465 suggested that pods reached greater maturity than 74% (inflection point). The results found in this study indicated a great potential to use high-resolution satellite images to predict peanut maturity variability in commercial field. In addition, the proposed method contributes to monitoring the dynamics spatio-temporal of maturity progression, allowing for more accurate in-season and inversion management strategies in peanut.