Explainable AI (XAI) for Agriculture
Abstract
In most nations, agriculture is the main industry providing employment. Agricultural activities used to be restricted to the cultivation of food and crops, but they have expanded over time to include the processing, production, marketing, and distribution of crops and livestock products. Agriculture related approaches or practices must be continuously reviewed with the goal of presenting innovative approaches to sustaining and improving agricultural activities. Currently, agricultural activities serve as the primary source of livelihood, increasing GDP, being one of the sources of national trade, reducing unemployment, and providing raw materials for production in other industries. Inadequate soil treatment, disease and pest infestation, among other issues, are only a few of the difficulties this industry must overcome in order to maximize productivity. There have been some difficulties with the increased use of technology in this industry, including the need for large amounts of data, low output, and the most obvious difficulty, the knowledge gap between farmers and technology. When compared to earlier more conventional methods, agricultural practices, and activities have significantly improved since technology entered the field. Technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) have been a few of the technologies that are widely used in these sectors with projects for improving crop production, disease prediction, continuous monitoring, efficient supply chain management, water waste and operational efficiency just to name a few but, this of this project will focus more on AI, more specifically on Explainable Artificial Intelligence (ExAI or XAI).
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XAI enhances weather forecasting accuracy by integrating complex weather data with historical patterns, allowing farmers to anticipate and mitigate the impact of adverse weather conditions on crops.
“XAI enhances weather forecasting accuracy by integrating complex weather data with historical patterns, allowing farmers to anticipate and mitigate the impact of adverse weather conditions on crops.”
From Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human (2025) · cites this paper as [91]
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- Authors
- Linheiro, E. S. M., Shinde, G. R., Mahalle, P. N., & Mirajkar, R.
- Affiliations
- Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
- Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
- Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
- Venue
- IoT and Big Data Analytics
- Publisher
- BENTHAM SCIENCE PUBLISHERS
- Published
- 2023-12-25
- Pages
- 161-176
- ISSN
- 2972-4155
- OpenAlex
- W4390269866
- Cited by
- 4
- References
- 18
- Citations by year
- 2025: 1 · 2024: 3
- FWCI
- 2.09
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- BENTHAM SCIENCE PUBLISHERS eBooks (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- explainable AI, agricultural technology adoption, crop production, plant disease forecasting, pest infestation, Internet of Things in agriculture, agricultural supply chain management, water wastage reduction
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