Although the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)-enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI-enabled IoTs have been used in different application environments, such as agriculture, where detection, prevention (if possible), and prediction of crop diseases, especially at the earliest possible stage, are desperately required. Bacterial stalk root is a common disease of tomatoes that severely affects its production and yield if necessary measures are not taken. In this article, AI and an IoT-enabled decision support system (DSS) have been developed to predict the possible occurrence of bacterial stalk root diseases through a sophisticated technological infrastructure. For this purpose, Arduino agricultural boards, preferably with necessary embedded sensors, are deployed in the agricultural field of maize crops to capture valuable data at a certain time interval and send it to a centralized module where AI-based DSS, which is trained on an equally similar data set, is implemented to thoroughly examine captured data values for the possible occurrence of the disease. Additionally, the proposed AI- and IoT-enabled DSS has been tested on benchmark data sets, that is, freely available online, along with real-time captured data sets. Both experimental and simulation results show that the proposed scheme has achieved the highest accuracy level in timely prediction of the underlined disease. Finally, maize crop plots with the proposed system have significantly increased the yield (production) ratio of crops.

中文翻译：

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人工智能和物联网支持的决策支持系统用于预测玉米作物细菌性茎根病

尽管物联网（IoT）被认为是最有前途的自动化各种日常生活活动（即监控和预测）的技术之一，但随着人工智能（AI）的引入和集成，它对于解决问题变得极其有用。 -启用智能学习方法。因此，由于其压倒性的特点，人工智能物联网已被用于不同的应用环境，例如农业，迫切需要农作物病害的检测、预防（如果可能）和预测，特别是在尽可能早的阶段。细菌性茎根病是番茄的常见病害，如不采取必要的措施，严重影响番茄的生产和产量。在本文中，人工智能和支持物联网的决策支持系统（DSS）被开发出来，通过复杂的技术基础设施来预测细菌性茎根病的可能发生。为此，Arduino农业板（最好配备必要的嵌入式传感器）被部署在玉米作物的农田中，以在一定的时间间隔捕获有价值的数据，并将其发送到一个集中模块，其中基于AI的DSS经过训练同样相似的数据集，用于彻底检查捕获的数据值以了解疾病可能发生的情况。此外，所提出的支持人工智能和物联网的 DSS 已经在基准数据集上进行了测试，即可以在线免费获取的数据集以及实时捕获的数据集。实验和仿真结果表明，所提出的方案在及时预测下划线疾病方面达到了最高的准确度。最后，采用该系统的玉米作物地块显着提高了作物的产量（生产）率。