Page 213 - AI for Good-Innovate for Impact

AI for Good-Innovate for Impact

50�2� Use-case Description

50�2�1� Description

Our use case focuses on revolutionising crop monitoring in agriculture by combining satellite 50-AIPARAGRO
imagery and artificial intelligence (AI). Traditional methods of crop monitoring are time-
consuming, labour intensive, and prone to human error, limiting farmers' ability to detect and
address issues in a timely manner. In leveraging satellite imagery and AI algorithms, we aim
to provide farmers with accurate and timely information to optimise their farming enterprises.

The use case addresses the problem of inefficient and inadequacy of crop monitoring methods.
Manual observation and sampling techniques are limited in their coverage and can miss crucial
information. This leads to reduced yields and economic losses due to undetected crop diseases,
nutrient deficiencies, or pest infestations. Our solution aims to overcome these limitations by
utilising satellite imagery and AI to provide a comprehensive and bird's eye view of crop fields.

The benefits are significant. Real-time and continuous monitoring of crop health across
large farming areas is made possible. This enables farmers to detect early signs of possible
challenges and take prompt action. Furthermore, AI algorithms can analyse complex satellite
imagery data and extract valuable insights that may not be apparent to the human eye. This
empowers farmers to make data-driven decisions regarding irrigation, fertiliser application,
and pest control, resulting in improved crop yields and resource management.

However, there are certain drawbacks to consider. The effectiveness of the AI-based approach
relies on the availability of high-quality satellite imagery and accurate ground truth data for
training the AI models. Limited access to such data, particularly in remote and underdeveloped
regions like Zimbabwe, can be a challenge. There are also initial setup and implementation
costs associated with acquiring satellite data and developing AI models, which may pose a
barrier for some farmers.

In conclusion, our use case aims to address the limitations of existing crop monitoring methods
by providing farmers with accurate and timely information. We aim to optimise farming, increase
yields, and contribute to sustainable food production. While there are challenges, the potential
benefits for farmers and the agricultural industry as a whole make it a promising solution for
improving crop monitoring and management.

UN Goals:

• SDG 1: No Poverty,
• SDG 2: Zero Hunger,
• SDG 8: Decent Work and Economic Growth,
• SDG 9: Industry, Innovation and Infrastructure,
• SDG 13: Climate Action,
• SDG 14: Life Below Water,
• SDG 15: Life on Land,
• SDG 17: Partnerships to achieve the Goal

Justification UN Goals selection: SDG 1 (No Poverty): The use of satellite imagery and AI in
crop monitoring enhances agricultural productivity and income opportunities for farmers.
By providing timely insights on crop health and management, the technology helps farmers
protect their crops from diseases and optimize yields, thereby reducing economic losses and

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