Wenjing  Xu ^1,2

1 Jiande Riyue Agricultural Development Co., Ltd., Jiande 311600, Zhejiang, China
2 Zhejiang Agronomist College, Hangzhou 310021, Zhejiang, China
Author    Correspondence author
Bioscience Methods, 2026, Vol. 17, No. 5   
Received: 15 Jul., 2026    Accepted: 19 Aug., 2026    Published: 02 Sep., 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Humidity management is a critical environmental factor influencing disease occurrence, crop health, and productivity in cucumber (Cucumis sativus L.) production systems. Excessive humidity, prolonged leaf wetness, and unstable microclimatic conditions create favorable environments for pathogen survival, spore germination, and infection, resulting in severe losses caused by fungal and bacterial diseases. With the rapid development of protected cultivation and precision agriculture technologies, humidity regulation has become an important strategy for sustainable disease prevention and improved crop performance. This review systematically summarizes the mechanisms underlying humidity-mediated disease development in cucumber production, focusing on the interactions among environmental humidity, plant physiological responses, and pathogen infection processes. The effects of humidity conditions on major cucumber diseases, including powdery mildew, downy mildew, gray mold, and bacterial diseases, are discussed from ecological and physiological perspectives. Furthermore, current humidity control approaches, such as ventilation management, irrigation optimization, dehumidification technologies, and integrated greenhouse climate regulation, are evaluated for their effectiveness in reducing disease risks. Advances in computational modeling, machine learning-based disease prediction, and sensor-driven environmental control systems are also highlighted as emerging tools for real-time disease forecasting and precision management. A case study framework is presented to demonstrate how humidity monitoring, disease assessment, and predictive modeling can be integrated to optimize greenhouse cucumber production. Despite significant progress, challenges remain in accurately defining humidity thresholds across cultivars and production environments, improving model interpretability, and integrating multi-factor environmental regulation strategies. Future research combining artificial intelligence, digital agriculture, and plant-microbe interaction studies will provide new opportunities for developing sustainable and intelligent disease management systems in cucumber production.

Humidity control; Cucumber production; Disease occurrence; Greenhouse microclimate management; Precision agriculture