Artificial intelligence and Internet of Things-enabled decision support system for the prediction of bacterial stalk root disease in maize crop - Al-Otaibi, Shaha; Khan, Rahim; Ali, Jehad; Ahmed, Aftab (Computational Intelligence, 2024)
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.
Keywords: Application environment; Common disease; Crop disease; Daily life activities; Data set; Embedded sensors; Maize crop; Problem-solving; Root disease; Technological infrastructure; artificial intelligence; decision support system; Internet of Things; maize crop