Overview

Project Highlights:

  • Addressing sustainability issues on the pressure to increase crop productivity
  • Earth observation based environmental monitoring tool development
  • Moving towards data-driven decision making for sustainable agriculture

Sugarcane production must address the challenge between the need to increase crop production and improve labour productivity whilst improving the efficiency of water and fertiliser use (Hess et al, 2016). One way to address this is to make effective use of data on geospatial patterns in cultivation and the factors that influence sugarcane yield to better understanding how sustainable production can be achieved.

Earth observation (EO) can play a key role in collecting this data, with existing research on the mapping of sugarcane (Adami et al, 2012), the determination of yield through the retrieval of bio-physical properties (Pereira et al, 2016) and measuring of water use efficiency (Olivier et al, 2018). However, these studies have been limited in scope and do not leverage the near continuous record of land surfaces changes over the last decade that EO data can provide. Integrating these high volume and velocity datasets with field level data on management, water use, cutting and sugar content will lead to new understanding of current production that can better inform the debate on sustainable sugarcane (figure).

This project will answer the question; how do we monitor sugarcane from long-term satellite programmes and new satellite constellations in near-real time? We will define how the emergence of cloud-based technologies, and enabling techniques, for analysing large quantities of EO data can be used for measurement over wide geographical areas at high resolution. The delivery of a robust tool for the monitoring of sugarcane will lead to greater understanding of the changing spatial and temporal patterns in cultivation and yield.

We have partnered with Neta Analytics (Neta), who work with the Indian Sugarcane sector to develop tools for data-driven decision making. They will provide field level expertise and ensure that the research outcomes will directly impact the sustainability of the sugarcane industry in India.

Sugarcane harvesting (in red) over a 12 day period in December 2018 for a location close to Nindra, Andhra Pradesh, India. Background image Sentinel1 multidate composite.

Methodology

An extensive sugarcane dataset will be built from long-term EO programmes, new satellite constellations, Neta Analytics databases, and available Indian land records. These data will be used to test new machine learning methods for the detection of sugarcane and the retrieval of biophysical parameters linked to crop development, yield development and stress (water, pests, and disease).

The outcome of the EO of sugarcane will be a compressive database of crop performance indicators linked with field level management data from Neta’s Indian sugarcane processors. This is will enable the development and evaluation of a scenario-based sugarcane decision support tool, incorporating yield models based on EO, weather, soil, and cane variety.

Training and Skills

Neta will provide additional training support for the student during placements in each year of the studentship. They will receive direct consultancy experience and benefit from targeted training in project management and applied informatics. They will work closely with Neta on the development of decision-making tools that are closely aligned with industry requirements.

Timeline

Year 1: technique familiarisation, data collection, Neta secondment.

Year 2: EO and crop model testing and validation, Neta secondment.

Year 3: Delivery, model scenario testing, tool development, thesis delivery, Neta secondment.

The standard model at Cranfield University is the production of peer-reviewed papers during the course of the studentship, in collaboration with our partner Neta. Neta placements will be for periods of between 1 week to 3 months in the UK office and India.

 

Partners and collaboration (including CASE)

This project has been co-developed with Neta, the industrial CASE partner and co-supervisor of the PhD studentship. The student will work closely with Neta staff in the UK and in India on the collection of ground data and the development of a decision support tool for sustainable sugarcane. They will benefit from their in-depth knowledge of the Indian sugarcane industry and access to the databases of sugarcane processors and links to the Coimbatore Sugarcane Institute. Direct working and targeted training will be achieved through placements at Neta offices in the UK and India.

Further Details

PI: Daniel Simms, Lecturer in Remote Sensing, Centre for Environmental and Agricultural Informatics (CEAI), School of Water, Energy and Environment, Cranfield University, Bedfordshire, MK43 0AL E: d.m.simms@cranfield.ac.uk T: 01234 750111 ex.2767

Co-I Academic: Paul Burgess, Reader in Crop Ecology and Management, Cranfield Soil and Agrifood Institute, School of Water, Energy and Environment, Cranfield University, Bedfordshire, MK43 0AL E: p.burgess@cranfield.ac.uk T: 01234 754291

Co-I Industry (Neta): Ramnath Nandakumar, Managing Director, Neta Analytics Limited E: ram@natems.co.in