Development of an Intelligent Fertigation Machine and Its Application Test in Soilless Cultivation of Mountain Blueberry
Conventional manual fertigation practices are generally limited by low control precision, considerable water and fertilizer waste, and poor adaptability to varied horticultural cultivation conditions. These limitations create particularly prominent challenges for high-value cash crops that are highly sensitive to root-zone substrate electrical conductivity (EC) and pH dynamics. To address these gaps, this study developed a digitally intelligent integrated fertigation system that integrates multi-sensor autonomous sensing, precise nutrient ratio regulation, and full-cycle operational monitoring modules. Systematic validation of both equipment performance and field production benefits was carried out via field plot experiments under typical mountainous soilless blueberry cultivation conditions. The test results showed that the developed fertigation system achieved a control uniformity of 95.28% for EC and 95.07% for pH. Under the prerequisite of identical total irrigation volume and total nutrient supply across the whole growing season, the precise control mode (PCM) of the proposed intelligent fertigation system exhibited significantly higher water use efficiency (WUE) and partial factor productivity of fertilizer (PFP) than the conventional simple fertigation management mode (SFM). Meanwhile, the unit-area yield of blueberries in the PCM treatment increased by 20.1%, and the commercial uniformity of individual fruit weight was significantly improved. This fully validated system can well meet the intelligent production requirements of mountainous soilless blueberry cultivation and provides a viable technical solution to support the intelligent upgrading of local soilless blueberry industries in Southwest China.
Authors
- Chunxiang Yang (ORCID: https://orcid.org/0009-0006-7261-3991)
Publication Details
- Journal
- Digital Intelligence in Agriculture
- Published
- 2026-11-09
- DOI
- https://doi.org/10.62762/dia.2026.142287
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00