Artificial Intelligence Reshaping Agricultural Knowledge Systems: Paradigm Shifts and Implementation Challenges

Abstract

As global agricultural digital transformation accelerates, data-driven technologies are reshaping traditional agricultural practices—yet their role in facilitating farmers’ knowledge evolution remains underexplored in existing research. This study focuses on three core digital tools to investigate how they transform the carriers and application of farmers’ knowledge. Empirical cases from agricultural fields demonstrate tangible impacts: smart drip irrigation systems optimized via reinforcement learning reduce water consumption, while large-scale smart agriculture deployments in sugarcane fields achieve 47% water savings and 20% fertilizer reduction. Convolutional neural networks also enable 3-day-ahead prediction of bovine mastitis, aiding early disease intervention. However, significant challenges persist, 78% of smallholder farmers cannot afford AI-driven equipment, provincial agricultural datasets remain isolated and unintegrated, and elderly traditional laborers face barriers to adapting to digital tools. Proposed solutions include low-cost smart equipment leasing, AI voice advisors tailored for lowliteracy farmers, and microcluster edge computers to address internet deprivation in remote areas. Additionally, the study identifies a critical dilemma: the risk of AI knowledge monopoly versus the potential for symbiosis between digital tools and traditional knowledge—resolving this requires prioritizing equity through building inclusive, accurate agricultural datasets and preventing algorithmic biases. It offers key insights to promote the inclusive adoption of digital tools, ensuring they empower rather than marginalize farmers in the digital agricultural transition.