Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning

Y Shendryk, R Davy, P Thorburn - Field Crops Research, 2021 - Elsevier
An accurate model for predicting sugarcane yield will benefit many aspects of managing
growth and harvest of sugarcane crops. In this study, Sentinel-1 and Sentinel-2 satellite …

Sensing management from space: predicting harvest dates

S Han, P Filippi, TFA Bishop - Precision agriculture'23, 2023 - wageningenacademic.com
The date of harvest is a management event critical to logistics, food availability, and
modelling at regional, national, or global scales. While traditionally collected in farmer …

Button Mushroom Farming Using Machine Learning

K Perera, R Packeeran, Y Suriyabandara… - Procedia Computer …, 2024 - Elsevier
Mushroom cultivation is getting increasingly popular within Sri Lanka's agricultural sector
due to its cost-effectiveness, high yield potential, and rising market demand. Nevertheless …

Assimilation of sentinel-1 change detection in the aquacrop model: case of sugarcane

J Wellens, M Stasolla, MT Sall… - … and Remote Sensing …, 2021 - ieeexplore.ieee.org
The “Compagnie Sucrière Sénégalaise”(CSS) wanted to upscale the field-level crop
simulation model AquaCrop (FAO's agro-meteorological model) for the automated …

Machine Learning-Based Sugarcane Yield Prediction Using Multispectral Time-Series Imagery

S Akbarian - 2023 - unsworks.unsw.edu.au
Accurate sugarcane yield prediction is important for the sugar industry in serving the
demands for decision-making systems such as harvest timing, product handling, and …

Using Sentinel-1 data for soybean harvest detection in Vojvodina province, Serbia

M Marković, B Živaljević, G Mimić… - Remote Sensing for …, 2023 - spiedigitallibrary.org
Information on crop harvest events has become valuable input for models related to food
security and agricultural management and optimization. Precise large scale harvest …