Automatic aphid counting based on deep learning and robotics technology in unstructured agriculture environments

Lead Research Organisation: University of Lincoln
Department Name: School of Computer Science

Abstract

Aims:
Aphids can cause direct damage and indirect virus transmission to crops. Timely monitoring the number of aphids can prevent the large-scale outbreak of aphids. However, the manual counting of aphids which is commonly used at present is inefficient, and it requires professional staff to complete it. Therefore, in this project, we will develop an automated aphid counting method combined with the agricultural robot and deep learning, so as to replace the manual aphid counting.

Objectives:
1. The robot can automatically navigate to the location of the yellow water pan traps device that collects aphids in the farmland and obtain images containing aphids.
2. Designing an aphid counting CNN to automatically count the number of aphids in the image.
3. The robot can remotely transmit the aphid counting results and the position coordinates of the corresponding yellow water pan trap to the customer service terminal.

Impacts:
Compared with the manual counting method, the developed automatic aphid counting method for agricultural robots will greatly improve the efficiency of aphid counting. At the same time, the designed automatic aphid counting CNN can be widely used in the identification and counting of agricultural small pests similar to aphids.

Planned Impact

The proposed CDT provides a unique vision of advanced RAS technologies embedded throughout the food supply chain, training the next generation of specialists and leaders in agri-food robotics and providing the underpinning research for the next generation of food production systems. These systems in turn will support the sustainable intensification of food production, the national agri-food industry, the environment, food quality and health.

RAS technologies are transforming global industries, creating new business opportunities and driving productivity across multiple sectors. The Agri-Food sector is the largest manufacturing sector of the UK and global economy. The UK food chain has a GVA of £108bn and employs 3.6m people. It is fundamentally challenged by global population growth, demographic changes, political pressures affecting migration and environmental impacts. In addition, agriculture has the lowest productivity of all industrial sectors (ONS, 2017). However, many RAS technologies are in their infancy - developing them within the agri-food sector will deliver impact but also provide a challenging environment that will significantly push the state of art in the underpinning RAS science. Although the opportunity for RAS is widely acknowledged, a shortage of trained engineers and specialists has limited the delivery of impact. This directly addresses this need and will produce the largest global cohort of RAS specialists in Agri-Food.

The impacts are multiple and include;

1) Impact on RAS technology. The Agri-Food sector provides an ideal test bed to develop multiple technologies that will have application in many industrial sectors and research domains. These include new approaches to autonomy and navigation in field environments; complex picking, grasping and manipulation; and novel applications of machine learning and AI in critical and essential sectors of the world economy.

2) Economic Impact. In the UK alone the Made Smarter Review (2017) estimates that automation and RAS will create £183bn of GVA over the next decade, £58bn of which from increased technology exports and reshoring of manufacturing. Expected impacts within Agri-Food are demonstrated by the £3.0M of industry support including the world largest agricultural engineering company (John Deere), the multinational Syngenta, one of the world's largest robotics manufacturers (ABB), the UK's largest farming company owned by James Dyson (one of the largest private investors in robotics), the UK's largest salads and fruit producer plus multiple SME RAS companies. These partners recognise the potential and need for RAS (see NFU and IAgrE Letters of Support).

3) Societal impact. Following the EU referendum, there is significant uncertainty that seasonal labour employed in the sector will be available going forwards, while the demographics of an aging population further limits the supply of manual labour. We see robotic automation as a means of performing onerous and difficult jobs in adverse environments, while advancing the UK skills base, enabling human jobs to move up the value chain and attracting skilled workers and graduates to Agri-Food.

4) Diversity impact. Gender under-representation is also a concern across the computer science, engineering and technology sectors, with only 15% of undergraduates being female. Through engagement with the EPSRC ASPIRE (Advanced Strategic Platform for Inclusive Research Environments) programme, AgriFoRwArdS will become an exemplar CDT with an EDI impact framework that is transferable to other CDTs.

5) Environmental Impact. The Agri-food sector uses 13% of UK carbon emissions and 70% of fresh water, while diffuse pollution from fertilisers and pesticides creates environmental damage. RAS technology, such as robotic weeders and field robots with advanced sensors, will enable a paradigm shift in precision agriculture that will sustainably intensify production while minimising environmental impacts.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023917/1 31/03/2019 29/09/2031
2601744 Studentship EP/S023917/1 30/09/2021 29/09/2025 Xumin Gao