Authors: Rahman M M, Muir J, Robson A J
The International Tri-Conference for Precision Agriculture in 2017
Accurate with-in season yield prediction is important for the Australian sugarcane industry as it supports crop management and decision making processes, including those associated with harvest scheduling, storage, milling, and forward selling. In a recent study, a quadratic model was developed from multi-temporal Landsat imagery (30 m spatial resolution) acquired between 2001–2014 (15th November to 31st July) for the prediction of sugarcane yield grown in the Bundaberg region of Queensland, Australia.
The resultant high accuracy of prediction achieved from the Bundaberg model for the 2015 and 2016 seasons inspired the development of similar models for the Tully and Mackay growing regions. As with the Bundaberg model, historical Landsat imagery was acquired over a 12 year (Tully) and 10 year (Mackay) period with the capture window again specified to be between 1st November to 30th June to coincide with the sugarcane growing season. All Landsat images were downloaded and processed using Python programing to automate image processing and data extraction. This allowed the model to be applied rapidly over large areas. For each region, the average green normalized difference vegetation index (GNDVI) for all sugarcane crops was extracted from each image and overlayed onto one time scale 1st November to 30th June. Using the quadratic model derived from each regional data set, the maximum GNDVI achieved for each season was calculated and regressed against the corresponding annual average regional sugarcane yield producing strong correlation for both Tully (R2 = 0.89 and RMSE = 5.5 t/ha) and Mackay (R2 = 0.63 and RMSE = 5.3 t/ha). Moreover, the establishment of an annual crop growth profile from each quadratic model has enabled a benchmark of historic crop development to be derived. Any deviation of future crops from this benchmark can be used as an indicator of widespread abiotic or biotic constraints. As well as regional forecasts, the yield algorithms can also be applied at the pixel level to allow individual yield maps to be derived and delivered near real time to all Australian growers and millers.
This article is published under license Creative Commons Attribution 4.0
Illustration Photo: Sugarcane field on Attakapas Canal west of Napoleonville, Louisiana, USA (credits: Louisiana Sea Grant College Program / Flickr Creative Commons Attribution 2.0 Generic (CC BY 2.0))