A System Architecture for Early Wilt Detection in Hydroponic Crops: An Implementation and Assessment

Abstract Hydroponic farming systems have a high risk of failure if the disaster is not mitigated and responded to quickly. Hydroponic plants only rely on mineral water that flows through their roots as a source of life; thus, any malfunction will have an immediate impact on the plant. If plant wilting is not recognized and not treated quickly, the plant will experience stress and have the potential to fail. To ensure that major disasters do not occur, an early warning mechanism is needed that can provide disaster information to farmers. In this study, an intelligent system architecture was built to detect premature wilt in hydroponic vegetable plants. It combines the capabilities offered by the OV2640 image sensor and ESP32-S system-on-chip, the Internet of Things, deep learning based on convolutional neural networks, and cloud computing to create a robust and low-cost remote plant condition monitoring solution. As a proof-of-concept, a prototype system is built, and performance tests are carried out to find out how robust and effective the proposed design is. Based on the results of the prototype test, it can be seen that the system built can run properly with an accuracy of wilting recognition of up to 90.90%, which was tested on samples of vertically grown mustard plants in a hydroponic greenhouse.