Development of Algorithms for an IoT‐Based Smart Agriculture Monitoring System

Sensor‐based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning‐based methods. To overcome these limitations, this research is aimed at proposing an IoT‐based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method‐convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison…