Innovative digital agriculture solutions for small agribusiness enterprises

Increasingly, researchers propose digital farming solutions that improve agricultural productivity and sustainability by better coordinating resource allocation and farm management under data-driven decision-making frameworks and attain higher economic returns for small-scale farmers. The need to leverage emerging technologies to develop and implement precision agriculture practices forces small agribusiness enterprises to reevaluate traditional farming techniques, operational efficiencies, and market accessibility in order to identify what technological interventions are relevant and how they will be enacted in various farming environments and agribusiness models. This is where this study aims to make a contribution, beyond introducing this research framework, which presents empirical and theoretical articles dealing with technological advancements, digital transformation, and socio-economic dynamics of smart farming, IoT applications, big data analytics, and mobile-based agricultural services. Following the research methodology, a quantitative and qualitative analysis of 250 small agribusiness enterprises is compiled to link digital adoption trends with agricultural profitability and sustainability. This article uses inferential techniques as an analysis tool to demonstrate the results associated with output and financial performance of agribusinesses and analyzes the farming practices and challenges to how agribusinesses can develop strategies needed for growth and become competitive. The stratified random sampling method can serve as a decision-making and strategic guide for determining respondents. This future research agenda provides ample scope for future empirical studies and applied science on precision agriculture, IoT-driven farming, financial viability of digital adoption, and rural technological empowerment.

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-05-07
DOI
https://doi.org/10.1051/bioconf/202517506007/pdf
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

Innovative digital agriculture solutions for small agribusiness enterprises

Springer Link (Chiba Institute of Technology)
Smart Agriculture and AI
article

Innovative digital agriculture solutions for small agribusiness enterprises

article en

Abstract

Increasingly, researchers propose digital farming solutions that improve agricultural productivity and sustainability by better coordinating resource allocation and farm management under data-driven decision-making frameworks and attain higher economic returns for small-scale farmers. The need to leverage emerging technologies to develop and implement precision agriculture practices forces small agribusiness enterprises to reevaluate traditional farming techniques, operational efficiencies, and market accessibility in order to identify what technological interventions are relevant and how they will be enacted in various farming environments and agribusiness models. This is where this study aims to make a contribution, beyond introducing this research framework, which presents empirical and theoretical articles dealing with technological advancements, digital transformation, and socio-economic dynamics of smart farming, IoT applications, big data analytics, and mobile-based agricultural services. Following the research methodology, a quantitative and qualitative analysis of 250 small agribusiness enterprises is compiled to link digital adoption trends with agricultural profitability and sustainability. This article uses inferential techniques as an analysis tool to demonstrate the results associated with output and financial performance of agribusinesses and analyzes the farming practices and challenges to how agribusinesses can develop strategies needed for growth and become competitive. The stratified random sampling method can serve as a decision-making and strategic guide for determining respondents. This future research agenda provides ample scope for future empirical studies and applied science on precision agriculture, IoT-driven farming, financial viability of digital adoption, and rural technological empowerment.

Springer Link (Chiba Institute of Technology)
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Smart Agriculture and AI
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