AI-enhanced IoT sensors for real-time crop monitoring: an era towards self-monitored agriculture

Smart farming is a rapidly growing field that uses sensors and internet of things (IoT) devices to collect and analyze data from farmlands and optimize crop production. This mini-ecosystem is potentially viable for gathering highly accurate information in real time. Still, continuously varying circumstances in the field pose the issues of self-degradation and noises during data collection. The current review covers different aspects of smart agriculture using artificially intelligent IoT platforms. Firstly, plant sensors for in vivo detections of plant signaling networks are briefly explained. Then there is a discussion that the data from various sensory devices for quantifying biomarker activities contain some noise which is refined using various algorithmic models. Proceedings further state that artificial intelligence (AI) helps to automate data acquisition with decreased human interference. Furthermore, different sensors are classified by their use in agriculture, with pros and cons for continuous monitoring and intelligent decisions. Plant wearable sensors have been discussed for their long-term data collection abilities from the field. Conclusively, plant wearable sensors are promising tools for smart agriculture, but they face some challenges such as biocompatibility, durability and scalability. From a future perspective, the current study comprehends the research gap to eliminate or reduce the issues of biodegradability and noise removal from the obtained data to perform more precise, continuous, and accurate data acquisitions remotely and smartly. Future research should focus on developing biodegradable microneedles, reducing the size of microneedles, creating biocompatible electrodes, and improving the performance of sensors in different climatic conditions.