GIS-Based Crop Type Classification and Land Use Analysis in Mauza Mustafabad, Pakistan
GIS-Based Crop Classification in Mauza Mustafabad
Keywords:
Keywords: Artificial Intelligence, Agricultural Marketing, Digital Agriculture, Farmers, Pakistan, Technology Adoption, Market ChallengesAbstract
This study examines the potential of Artificial Intelligence (AI) to address agricultural marketing challenges faced by farmers in Faisalabad, Pakistan. It evaluates farmers’ awareness, readiness, and willingness to adopt AI technologies while identifying key socioeconomic, technical, and institutional factors influencing adoption. Primary data were collected through a structured questionnaire from 200 purposively selected farmers, and analyzed using descriptive statistics, Spearman’s correlation, and binary logistic regression.
Results showed that nearly two-thirds of farmers were willing to adopt AI-based tools and platforms, provided sufficient training, guidance, and digital infrastructure were available. Education, digital literacy, internet access, and trust in technology emerged as significant determinants of AI adoption. Conversely, inadequate infrastructure, high costs, limited access to affordable technology, and weak institutional support remained major barriers.
The study concludes that targeted policy measures promoting digital literacy, expanding rural internet access, and providing financial and institutional incentives can substantially improve AI adoption in agricultural marketing. Strengthening digital infrastructure and enhancing farmers’ technological capabilities are therefore essential for Pakistan’s transition toward data-driven, efficient, and sustainable agricultural markets.
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Data Availability Statement
The data used in this study were collected directly by the author through field surveys. Due to privacy and confidentiality considerations of the respondents, the data are not publicly available but can be provided by the corresponding author upon reasonable request.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in AgriPaT are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
