ABSTRACT:
Africa’s population is expected to reach about 2.6 billion by 2050. This will require an increase in food production by up to 70% to fit the need of the population, a serious challenge for the agriculture and food systems. Such requirement, in a context of resource scarcity, climate change, COVID-19 pandemic, and very harsh socioeconomic conjecture, may be easier to attain with the application of emerging technologies and innovations such as artificial intelligence (AI) to leapfrog the transformations required in the sector.
The main objective of this initiative is to advance the responsible development, deployment, and scaling of homegrown AI research and innovations to tackle pressing challenges in agriculture and food systems in Africa. This will be accomplished through setting-up, managing, and supporting an innovation research network on AI for agriculture and food systems.
This network will consist of 6-10 innovation research projects that will develop, deploy, test, and seek to scale responsible and African-led artificial intelligence research and innovations. This research will deepen our understanding of how to develop, deploy, and scale responsible AI innovations for sustainable agriculture and food systems in Africa. The project will also seek to use these lessons learned to inform African and international AI policy and practice conversations.
LOCATION
Nigeria and Ghana, The African Technology Policy Studies Network (ATPS), ICIPE and Kumasi Hive
PRINCIPAL INVESTIGATOR:
- Nicholas Ozor
SUBPROJECTS
| Project 1 — Monitoring and Artificial Intelligence Tools for Smart Agriculture | |||||
| Implementation Country: Cape Verde | Language: Portuguese | Project Amount: USD 50,000 | |||
| Principal Investigator: Dr Sonia Semedo Universidade de Cabo Verde | Partners: — | ||||
| Project Summary In a bid to increase food production amid growing drought concerns, this project intends to study the feasibility of using emerging technologies like IoT and AI on existing farming practices and develop sustainable strategies increase efficiency and effectiveness of agricultural production. | |||||
| Project 2 — Development of Machine Learning Model for Crop Pests and Diseases Diagnosis Based on Crop Imagery Data | |||||
| Implementation Country: Tanzania | Language: Swahili | Project Amount: USD 51,000 | |||
| Principal Investigator: Dr. Hudson Laizer, Mbeya University of Science and Technology, Mbeya, Tanzania | Partners: DevData Analytics Limited, Tanzania, and Southern Corridor Alliance of Agriculture Producers | ||||
| Project Summary Common beans and Irish potatoes are among the most important food and cash crops for small scale farmers in Tanzania, but their yield is threatened by four diseases. Cutting off diseased leaves and plants help curb the spread of these diseases, making early detection important. This project aims to develop an ML model that will be able to detect the diseases earlier based on leaf imagery data and enable the farmer to make the appropriate decision for managing the spread of the diseases. | |||||
| Project 3 — Enhancing Farm-scale Crop Yield Prediction using Machine Learning for Internet of Agro – Things in Tanzania | |||||
| Implementation Country: Tanzania | Language: Swahili | Project Amount: USD 51,000 | |||
| Principal Investigator: Dr. Barakabitze Alcardo Alex, Sokoine University of Agriculture, Morogoro, Tanzania | Partners: Dr. Dominic Ringo Research, Community, and Organisational Development (RECODA), Mr. Adam Rowland SAHARA Ventures | ||||
| Project Summary In spite of agriculture being the largest and most important sector of the Tanzanian economy, many farmers and other sector stakeholders face considerable challenges in increasing their yields. This is because they struggle to access economically viable technology. This project will use ML techniques to learn useful patterns from input data to provide models that farmers can use to predict crop yield or offer recommendations based on the seasons. | |||||
| Project 4 — Using Artificial Intelligence to enhance the Production, Management and Marketing of Nsukka Yellow Pepper (Capsicum Chinense Nsukkadrilus) | |||||
| Implementation Country: Nigeria | Language: English | Project Amount: USD 59,000 | |||
| Principal Investigator: Engr Prof. Chinenye Anyadike APWEN NSE Building, Victoria Island, Lagos, Nigeria | Co-PI’s: Mr. Alex Onyia, Educare, Dr. Nwobodo Cynthia Ebere, University of Nigeria, Nsukka | ||||
| Project Summary Nsukka yellow pepper is one of the varieties of pepper grown in Nigeria and its popularity has attracted stakeholders to improve and sustain its production. However, this production is faced with serious challenges like pest attacks, high costs of inputs and low profit margins due to middlemen in marketing. To help overcome these bottlenecks, this project aims to leverage AI tools and applications to collect datasets for early pest detection, provide support for early soil nutrient loss detection, improve water conservation and improve access to the market, thus increasing its value chain. | |||||
| Project 5 — Scaling Smartphone-based tools for Early Crop Detection & Monitoring | |||||
| Implementation Country: Uganda | Language: English | Project Amount: USD 55,000 | |||
| Principal Investigator: Dr. Owomugisha Godliver, Busitema University, Tororo, Uganda. | Co-PI’s: Emmanuel Ofumbi, Papoli Community Development Foundation, Tororo, Uganda Estefanía Talavera, University of Twente, Netherlands | ||||
| Project Summary This research proposes to tackle one of the most challenging problems in agriculture; the detection and diagnosis of crop disease in the field by using low-cost smartphones with embedded assisted technologies. The goal is to elevate early crop disease and pest surveillance and diagnostic capabilities in the hands of cassava smallholder farmers at scale. | |||||
| Project 6 — Pest Occurrence Early Warning System and Diagnostic Tool Development using Geoinformation and Artificial Intelligence. A case study of Tomato Leaf Miner(Tuta Absoluta) and Whiteflies in Machakos Kenya | |||||
| Implementation Country: Kenya | Language: English | Project Amount: USD 52,000 | |||
| Principal Investigator: Dr. Hilda Manzi, Geospatial Research International, Nairobi, Kenya | Co-PI’s: Cosmus Muli, Kathaana Vegetable Growers Dr. Joseph Sang, Jomo Kenyatta University of Agriculture and Technology | ||||
| Project Summary The Kenya Horticulture sub-sector is the largest in agriculture contributing 33% of the agricultural GDP. The threats of climate change have, however, affected both the productivity and profitability of the sector. Increasing temperatures and changes in atmospheric moisture have resulted in the emergence of new pests as well as an upsurge of existing ones. Despite its importance, tomato, an important crop in Kenya, is constrained by pests and diseases accounting for 80-100% losses, with the most common pests being tomato leafminer (Tuta absoluta) and white flies. This research intends to develop an AI-based spatial tool for the monitoring and surveillance of Tuta absoluta and whiteflies on tomato crops in Machakos County, Kenya. | |||||
| Project 7 — Empowering Smallholder Farmers (SHF) in Busia County using Low-Cost IoT (Internet of Things) and AI (Artificial Intelligence Tools.) | |||||
| Implementation Country: Kenya | Language: English | Project Amount: USD 53,000 | |||
| Principal Investigator: Dr. Betsy Muriithi, Strathmore University, Nairobi, Kenya. | Co-PI’s: Dr. Joseph Wabwire Masinde, Centre for Enterprising Communities (CECO), and Karen Basiye, Safaricom DigiFarm, Kenya. | ||||
| Project Summary Smallholder farmers who produce more than 70% of the food consumed in Kenya lack access to localised productivity maximising data and are extremely incapacitated by severe changes in weather. AI and IoT tools can offer a solution to these farmers, coupled by the rapid development of low cost devices that can support these technologies. Using an IoT-based tool with enhanced AI features and plugins, this team intends to test the viability of strengthening local economies through the provision of access to vital weather data that would enhance crop yield. | |||||
| Project 8 — Building the artificial intelligence (AI) for soil moisture and nutrient monitoring under irrigated agriculture among smallholder farmers, academic and agriculture experts in Malawi | |||||
| Implementation Country: Malawi | Language: English | Project Amount: USD 54,000 | |||
| Principal Investigator: Dr. Isaac Fandika, Department of Agricultural Research Services, Chikwawa, Malawi. | Co-PI’s: Mr. Alinafe Emannuel Kaliwo, MECHRO Ltd, and Ms. Pennia Mavedzenge, World Vision International | ||||
| Project Summary Irrigation has been one of the leading solutions to climate change and population growth challenges to food security in sub–Saharan Africa. However, it has failed to live up to its potential. There is a need to build institutional arrangements that integrate the public and private sector players in AI soil moisture and nutrient monitoring tools in Malawi. This project’s goal is to scale up sensor technology for soil water and nutrient monitoring in irrigated agriculture. It is expected that a public-private partnership will be set – up to be developing and deploying AI tools for monitoring soil moisture and nutrient in irrigated farming covering 660 farmers at 30 irrigation schemes. | |||||
| Project 9 — TOLBI AI, an AI-based digital tool for smart, sustainable, and efficient agriculture. | |||||
| Implementation Country: Senegal | Language: French | Project Amount: USD 48,000 | |||
| Principal Investigator: Mouhamadou Kebe, Corniche Ouest, Dakar, Senegal. | Co-PI’s: Mouhamadou Moustapha Cissé, African institute for Mathematical Sciences Cheikh Ahmadou Bamba Fall, Yessal Agrihub | ||||
| Project Summary In sub-Saharan Africa, losses incurred as a result of non-adapted agricultural practices to climate change are 30% (plant health management, fertilisation, irrigation) and the associated post-harvest losses are estimated at 4 billion US dollars. With the development of Climate Smart Agriculture, the main objective of the Tolbi AI project is to use a combination of Artificial Intelligence, satellite images, and local languages to provide small-scale agriculture producers and inform national agricultural policies with real-time information on yield forecasts using a field management platform that monitors plant health, fertilisation and water needs. | |||||
| Project 10 — Detection of Crop Pests and Diseases on Web and Mobile Devices using Deep Learning. | |||||
| Implementation Country: Ghana | Language: English | Project Amount: USD 49,000 | |||
| Principal Investigator: Dr. Patrick Mensah, Department of Computer Science, University of Energy and Natural Resources, UENR, Ghana | Co-PI’s: Mr. Suweidu Abdulai, Ghana Developing Communities Association, and Mr. Francis Ata Amponsah DIGILECT SYSTEM | ||||
| Project Summary The community around the University of Energy and Natural Resources in Ghana is highly dependent on agriculture for their livelihood. However, due to scarcity of land, the farmers cultivate crops on small pieces of land and these crops are regularly infested by pests and diseases. This project aims to develop a deep learning based mobile and web app to efficiently detect cassava, maize, tomatoes and cashew pests/diseases. Due to high illiteracy rates in the farming communities, their AI system will be user-friendly and have a text-to-voice facility to communicate the results and recommendations in English and the popular local language “Twi”. This is to also facilitate easy usage by the visually impaired. | |||||