Spatio-temporal Tensor Multi-Task Learning for Precision Fertilisation with Real-world Agricultural Data

TL;DR: In this paper , a tensor based approach was proposed to predict the amount and time of base fertiliser and topdressing in real-world agricultural data, where realworld agricultural measurements were encoded into a three-dimensional tensor, and a set of interpretable temporal and spatial latent factors was extracted from the raw data through tensor decomposition.

Abstract: Precision fertilisation is the application of target variable fertilisation techniques based on soil fertility variations in specific regions. Precise fertilisation can help to balance soil nutrients, conserve fertiliser, prevent pollution, and boost crop yields. The lack of agricultural data is a key reason limiting the application of machine learning methods in agriculture. Due to the low-level network technology in farms, it is difficult to obtain diverse and complete agricultural data. The existing agricultural data is typically unstructured and difficult to mine. In this article, we extracted real-world agricultural dataset from four real farms with winter wheat and it includes different types of factors describing agriculture, such as climate, soil nutrients, crop yield information. Moreover, we present a novel multi-task learning (MTL) approach based on a tensor built of farm data to efficiently prediction both the amount and time of base fertiliser and topdressing. Specifically, real-world agricultural measurements (such as climate data, soil nutrients, etc.) are encoded into a three-dimensional tensor, and a set of interpretable temporal and spatial latent factors is extracted from the raw data through tensor decomposition. The latent factors are then utilised to train the spatio-temporal tensor prediction model. We have conducted extensive experiments utilising the real-world agricultural dataset. The experimental results show that our proposed methods have superior accuracy and stability in fertilisation prediction compared to state-of-the-art regression methods.