Nawshad Nadim, Ali Md Asraf, Fazira Ku Nurul, Ahmad R Badlishah, Ahammad Mejbah, Ahmed Nasim
Department of Computer Science, American International University - Bangladesh, Dhaka, Bangladesh.
Artificial Intelligence Research and Innovation Lab - AIRIL, Dhaka, Bangladesh.
PLoS One. 2025 Sep 8;20(9):e0330488. doi: 10.1371/journal.pone.0330488. eCollection 2025.
Due to limited literacy among root-level farmers, hydroponic farming in Bangladesh faces significant challenges. Therefore, there is a demand for easy-to-use technical systems to help farmers to monitor and operate smart systems. To address the issue, this study introduces a robust hydroponic system that provides automatic guidelines, monitoring, and a disease detection system. The main objective of this paper is to support farmers by making the cultivation process more convenient and less stressful. The system is structured into three phases: hardware implementation using WeMos controllers, disease detection using the Deep Learning model, and mobile application development for sensor data analysis and automatic notifications. The proposed system significantly demonstrates a high disease detection accuracy of 98.5%. Moreover, the survey report shows that around 80% of the root-level farmers find the system helpful for their cultivation process and increase the usability and monitoring of the system. These findings suggest that the proposed system can substantially improve the operational efficiency and sustainability of hydroponic farming, and it has the potential to enable more effective resource management and disease prevention strategies.
由于基层农民识字率有限，孟加拉国的水培农业面临重大挑战。因此，需要易于使用的技术系统来帮助农民监测和操作智能系统。为解决这一问题，本研究引入了一种强大的水培系统，该系统提供自动指导、监测和疾病检测系统。本文的主要目的是通过使种植过程更便捷、压力更小来支持农民。该系统分为三个阶段：使用WeMos控制器进行硬件实现、使用深度学习模型进行疾病检测以及开发用于传感器数据分析和自动通知的移动应用程序。所提出的系统显著展示了98.5%的高疾病检测准确率。此外，调查报告显示，约80%的基层农民发现该系统对他们的种植过程有帮助，并提高了系统的可用性和监测能力。这些发现表明，所提出的系统可以大幅提高水培农业的运营效率和可持续性，并且有潜力实现更有效的资源管理和疾病预防策略。
