TMTCIL-st: Spatio-Temporal Tensor Multi-Task Continuous Incremental Learning for Multi-Fertiliser Management

Precision fertilisation is essential for fertiliser conservation, soil nutrient balancing, emission reduction and yield improvement. Precision fertilisation prediction refers to the simultaneous prediction of timing and rate for fertilisation. Current research focuses on the precision prediction for a single type of fertiliser, or the application rate prediction of multiple fertilisers. However, interactions of multiple fertilisers can affect plant growth, soil structure and environmental balance. The lack of corresponding agricultural data and the resulting barriers to designing temporal and spatial fertilisation prediction algorithms are significant obstacles to utilising machine learning approaches for precision application prediction of multiple fertilisers. In this research, we investigated and collected real-world agricultural dataset from ten genuine winter wheat farms in England, which encompassed a variety of agricultural variables including climate, soil, cropping data and multiple fertilisations. Based on this dataset, we proposed a novel spatio-temporal tensor multi-task learning approach to deal with the spatio-temporal characteristics and scarcity of agricultural data. The approach utilises the multidimensional tensor created from the raw data to efficiently predict the timing and rate of base and topdressing fertilisation of multiple fertilisers (nitrogen, phosphorus, potassium and sulphur). As there are complex interactions among different fertilisers, we constructed the continuous incremental learning framework based on this algorithm to enhance the prediction performance of precision application for multiple fertilisers. Extensive experiments were conducted to evaluate the performance of our proposed approach compared to standard regression models with real-world agricultural dataset. The results demonstrated that our proposed approach provides superior accuracy and stability in the precision application prediction of multiple fertilisers.