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Crop Type Prediction Utilising a Long Short-Term Memory with a Self-Attention for Winter Crops in Australia

The precise mapping and monitoring of crop types is essential for forecasting food production during each crop growing cycle. Accurate monitoring and discriminating of crop types will enable industry to make more informed decisions, thus reducing input costs and improving on-farm profitability. Multiple remote sensing and machine learning derived approaches have attempted to solve this challenge. Generally these approaches fail to accurately capture the biological processes (e.g., phenology) of crop growth due to lack of temporal information capture in their dataset. To address this problem, we propose a deep learning architecture in which a long short-term memory (LSTM) is incorporated with a selfattention mechanism. Experimental results demonstrate high accuracy (approaching or exceeding 90% for full season) in discriminating between six crop species for different environments in Australia. Importantly, most species could be identified at >80% precision within approximately two to three months of planting (end June), though to achieve this discrimination for Wheat and Barley required data to 15th October. Nevertheless, this precision is sufficient to inform end-of-season planning for yield and logistics relating to specific crop species.