Embracing of Artificial Intelligence of Things (AIoT) in Regulated Agricultural Components Towards Enhancing Agricultural Productivity and Sustainability in Nigeria

Abstract

The Northern region of Nigeria produces major revenue from agriculture, thereby contributes 23% of Global GDP despite of constrained land resources. The improvement in GDP can be made realizable with the incorporation of systematic, organized and sustainable smart irrigation system In this chapter, we are attempting the realization of IoT (Internet of Things) as well as AI (Artificial Intelligence) often termed as AIoT, to transform the traditional agricultural system in Northern Nigeria into smart and sustainable agriculture. Herein we are highlighting controlled and selective usage of Agrochemicals and automatic monitoring of chemical usage in agriculture with the help of AIoT to avoid chemical wastage. Machine learning appended IoT in irrigation system are described to protect crop health from climate change and other non-negotiable parameters such as population growth, employment and food security issues. Soil management with the help of AI is also discussed in this chapter. Herein, we have analyzed the pros and cons of present Nigerian agriculture especially for the northern side and presented possible AIoT based approaches like AI workshops and training to farmers, soil testing using AI, quality of chemicals testing, optimization of chemicals usage, environmental factors predictions, decision making with the help of Operations Research (OR).

Introduction

Background

Agriculture, one of humanity's oldest professions, remains essential for survival. Irrigation plays a critical role in crop growth, as water availability directly affects soil health and productivity. In places like Western Africa, over-reliance on unpredictable rainfall hinders sustainable farming. Nigeria is responding by modernizing agriculture, with a focus on efficient irrigation systems to boost yields, reduce water waste, and support economic growth.

Irrigation and Technology

Irrigation is a time-tested agricultural practice essential for crop cultivation, especially in arid regions or where rainfall is insufficient. Today, automatic irrigation systems, powered by IoT and sensors, are replacing traditional methods. These systems use soil moisture, weather, and temperature data to control water flow, conserve resources, and reduce labor. Technologies like subsurface drip irrigation, soil moisture sensors, and GSM modules allow farmers to monitor and control irrigation remotely, improving accuracy and efficiency.

AI and IoT in Agriculture

AI and IoT (AIoT) are transforming agriculture. Smart systems can monitor, analyze, and manage farm activities through real-time data. Applications include crop spraying using drones (UAVs), automated irrigation, and predictive modeling. These technologies reduce labor, increase precision, and improve productivity.

Artificial Intelligence in Irrigation

AI and machine learning (ML) enhance irrigation by analyzing data to make predictions and optimize water usage. ML algorithms assess factors like weather, soil conditions, and crop needs to automate irrigation decisions. Common ML techniques include decision trees, regression, clustering, and neural networks. These tools help forecast yields, detect diseases, and recommend optimal farming practices.

Productivity and Sustainability

AI increases agricultural productivity by reducing costs, improving forecasting, and minimizing environmental impact through precision agriculture. However, there's concern about job loss and the diminishing role of traditional farming knowledge.

AIoT Across Agricultural Stages

AIoT tools are used across all phases of farming—pre-planting (e.g., soil and irrigation analysis), planting (e.g., weather and disease forecasting), and post-planting/processing (e.g., storage and distribution management). These tools support data-driven decisions, helping to enhance crop yield, resource use, and farm sustainability.

Conclusion

Consequently, this research progresses agriculture. Initially, the study contributes to the irrigation literature by highlighting the significance of AI in tackling sustainability concerns in irrigation. Secondly, the paper highlights a topic that has historically gotten little attention in agricultural science. Given the significance of agriculture for food security, the world\'s current food crises and environmental problems can be solved in unique ways using artificial intelligence (AI) and the Internet of things (AIoT). Additionally, understanding how AI and AIoT are used and accepted in the agriculture sector might help with the development of AI solutions for other, related industries. In conclusion, agricultural artificial intelligence (AI) is still in its infancy, but there is great potential for this technology to advance and be able to address major environmental challenges alongside critical crop yield difficulties.

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Copyright

Copyright © 2025 Kabiru Hamisu, Soumik Podder, SulaimanAuwaluYaro , HafsatSanusi Mohammed, AbdulnasirLawan Isah, Ahmad Usman Shuaib, MuhsinShafiu Umar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.