AI与大数据驱动的《作物栽培与种子检验》实验教学改革探索

AI and Big Data‑Driven Experimental Teaching Reform in “Crop Cultivation and Seed Inspection”: An Exploration

摘要: 《作物栽培与种子检验》是生物科学连接理论与生产的重要实践课，实验教学对培养动手、科学思维与职业素养关键。AI与大数据变革种业，传统教学存在内容、技术、评价等方面的滞后与脱节。基于信阳师院实际，系统剖析问题并提出AI改革：构建三层次递进体系，引入机器视觉、近红外和智能传感等现代技术，开发虚拟仿真平台与数字化资源，建立多模态过程评价机制。以形态识别和纯度鉴定两案例展示实施路径，预期提升检测效率与数据分析能力。本研究拟在后续1~2个教学周期内开展试点，通过前测–后测对比、问卷调查、学生访谈及作品分析等方式收集实证数据，以验证改革方案的有效性，并对预期成果进行量化分析。旨在为地方高校农学实验教学数字化提供可操作可推广参考。

Abstract: “Crop Cultivation and Seed Inspection” is a key practical course bridging theory and production in biological sciences; its laboratory teaching is essential for developing hands-on skills, scientific thinking, and professional ethics. As AI and big data transform the seed industry, traditional teaching suffers from lagging content, technological disconnect, and monolithic assessment. Based on the practice at Xinyang Normal University, this paper systematically analyzes these problems and proposes an AI‑driven reform: constructing a three‑tier progressive system (basic verification - comprehensive design - inquiry innovation), introducing modern technologies such as machine vision, near‑infrared spectroscopy, and intelligent sensing, developing a virtual simulation platform and digital resources, and establishing a multimodal process‑oriented evaluation mechanism. Two cases—seed morphological identification and purity determination—are used to demonstrate the implementation pathway, with expected outcomes including improved detection efficiency and data analysis capability. This study plans to pilot the proposed reform over the next 1~2 teaching cycles, and will collect empirical data through pre-test/post-test comparisons, questionnaires, student interviews, and work analysis to validate the effectiveness of the reform and to quantitatively analyze the expected outcomes. This study aims to provide a practical and replicable reference for the digital transformation of agronomy laboratory teaching at local universities.