Digital Intelligence in Agriculture
ISSN: 3069-3187 (Online)
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TY - JOUR AU - Wei, Yongqiang AU - Jiang, Xuyan AU - Liu, Chang AU - Li, Rui PY - 2025 DA - 2025/12/29 TI - Research on Adaptive Improvement and Promotion Path of Intelligent Agricultural Machinery in Hilly and Mountainous Areas JO - Digital Intelligence in Agriculture T2 - Digital Intelligence in Agriculture JF - Digital Intelligence in Agriculture VL - 1 IS - 2 SP - 110 EP - 119 DO - 10.62762/DIA.2025.914623 UR - https://www.icck.org/article/abs/DIA.2025.914623 KW - intelligent agricultural machinery KW - hilly and mountainous areas KW - terrain adaptability KW - path planning KW - sharing service model KW - farmland mechanization-friendly transformation AB - Hilly and mountainous areas account for 69% of China’s land area and undertake critical agricultural production tasks, but the poor adaptability of mainstream intelligent agricultural machinery and inefficient promotion models have become key bottlenecks restricting agricultural modernization. This study’s core innovation lies in constructing a ``four-dimensional integrated solution'' (equipment lightweight improvement - dynamic control optimization - hybrid sharing promotion - farmland mechanization-friendly transformation) and quantifying the coupling mechanism between topographic constraints and agricultural machinery performance. By introducing plot shape coefficient and slope volatility into the Terrain Adaptability Index (TAI), a lightweight intelligent harvester with 32% weight reduction and stable operation on 25° slopes was developed, and a Beidou RTK-based dynamic path planning system was also designed. Field tests in Hunan and Chongqing showed that the integrated solution increased operation efficiency by 250% and reduced costs by 59.3% compared with traditional methods. This research provides a systematic technical paradigm and promotion strategy for agricultural intelligence in hilly and mountainous regions worldwide. SN - 3069-3187 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Wei2025Research,
author = {Yongqiang Wei and Xuyan Jiang and Chang Liu and Rui Li},
title = {Research on Adaptive Improvement and Promotion Path of Intelligent Agricultural Machinery in Hilly and Mountainous Areas},
journal = {Digital Intelligence in Agriculture},
year = {2025},
volume = {1},
number = {2},
pages = {110-119},
doi = {10.62762/DIA.2025.914623},
url = {https://www.icck.org/article/abs/DIA.2025.914623},
abstract = {Hilly and mountainous areas account for 69\% of China’s land area and undertake critical agricultural production tasks, but the poor adaptability of mainstream intelligent agricultural machinery and inefficient promotion models have become key bottlenecks restricting agricultural modernization. This study’s core innovation lies in constructing a ``four-dimensional integrated solution'' (equipment lightweight improvement - dynamic control optimization - hybrid sharing promotion - farmland mechanization-friendly transformation) and quantifying the coupling mechanism between topographic constraints and agricultural machinery performance. By introducing plot shape coefficient and slope volatility into the Terrain Adaptability Index (TAI), a lightweight intelligent harvester with 32\% weight reduction and stable operation on 25° slopes was developed, and a Beidou RTK-based dynamic path planning system was also designed. Field tests in Hunan and Chongqing showed that the integrated solution increased operation efficiency by 250\% and reduced costs by 59.3\% compared with traditional methods. This research provides a systematic technical paradigm and promotion strategy for agricultural intelligence in hilly and mountainous regions worldwide.},
keywords = {intelligent agricultural machinery, hilly and mountainous areas, terrain adaptability, path planning, sharing service model, farmland mechanization-friendly transformation},
issn = {3069-3187},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
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