Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data

Abstract

This study investigates the transformative impact of adaptive Artificial Intelligence (AI) on precision agriculture, focusing on optimizing farm operations through real-time data analysis. The primary objective was to assess how adaptive AI technologies enhance the efficiency, productivity, and sustainability of agricultural practices. Employing a systematic literature review and content analysis, the methodology involved scrutinizing peer-reviewed articles and grey literature from key databases, applying stringent inclusion and exclusion criteria to ensure relevance and quality. Key findings reveal that adaptive AI significantly improves farm operations by enabling precise monitoring and management of crops, soil, and environmental conditions. The integration of IoT devices and machine learning algorithms facilitates real-time data analysis, leading to optimized resource use, reduced environmental impact, and increased crop yields. Economic benefits include cost savings through efficient resource management, while environmental advantages encompass minimized chemical use and enhanced sustainability. Challenges identified include high implementation costs, technical complexity, and data privacy concerns. However, solutions such as policy support, technological advancements, and stakeholder collaboration are proposed to overcome these barriers. Lastly, adaptive AI holds the potential to revolutionize precision agriculture by making it more efficient, sustainable, and productive. Future research should focus on developing accessible, robust AI solutions and fostering an environment conducive to technological adoption. The study underscores the need for continued innovation and policy support to fully realize the benefits of AI in agriculture.