Sorghum grain yield estimation based on multispectral images and neural network in tropical environments

• The model showed better quality of estimating yield in the initial stage of sorghum development. • The topographic data of the land obtained directly by the harvester were used as an input parameter in the model. • Wide Dynamic Range Vegetation Index as an input parameter in the model promotes greater accuracy and precision. • The study demonstrates that the dry-down process decreases the near-infrared reflectance, making the Visible Atmospherically Resistant Index better correlated with the more advanced stages of the culture. The application of machine learning and remote sensing techniques to estimate crop yield has garnered significant attention due to their ability to analyze large volumes of data and combine various inputs for enhanced results. This study aimed to optimize sorghum grain yield prediction in tropical conditions over two seasons by integrating vegetation indices and soil elevation data to calibrate Artificial Neural Networks (ANNs). We developed and implemented ten ANNs with a Multilayer Perceptron architecture using the Keras library, incorporating vegetation indices (CIgreen, SR, VARI, WDRVI) from the PlanetScope platform and soil elevation data from a harvester machine. The general M2 model (CIgreen + SR + VARI + WDRVI + soil elevation) achieved the highest performance with an R 2 of 0.89 and RMSE of 0.22 t ha⁻¹ at 30 days after sowing (DAS). In 2019, the M9 model (CIgreen + SR + WDRVI + soil elevation) performed best at the same growth stage with an R 2 of 0.82 and RMSE of 0.27 t ha⁻¹. Conversely, in 2020, the M3 model exhibited the best performance at 120 DAS, with an R 2 of 0.84 and RMSE of 0.15 t ha⁻¹. These results highlight the variability in model performance due to environmental factors, plant growth dynamics, and the suitability of specific indices at different growth stages and years. Despite that the general model performed similarly to the growth stage-specific models, suggesting its potential applicability across different conditions and cultivars for estimating sorghum yield in tropical environments.