Design and implementation of IoT sensors for nonvisual symptoms detection on maize inoculated with Exserohilum turcicum

Diseases on maize crops are highly caused by chronic and emerging pathogens that results in stagnant growth in the plant system. Several initiatives have been adopted to manage disease on crops which include new cultivation practices, genetic engineering, plant breeding and chemical control which have only proven to perform better on laboratory-based approaches. Meanwhile, small holder farmers can hardly afford such intervention mechanisms because they are costly and require highly skilled labor. With the advancement of technologies in Internet of Things (IoT) and different artificial intelligence models, non-visual signs of disease are being explored and experimented in this work for nonvisual early disease detection purposes. Volatile Organic Compounds (VOCs), Ultrasound, Nitrogen, Phosphorous, Potassium (NPK) fertilizer are profiled on control maize and inoculated maize with Exserohilum turcicum fungus to generate time series data. Dataset generated are preprocessed, analyzed, and visualized using pandas and matplotlib python tools. Machine Learning algorithms have been inferenced on the dataset; Statsmodel for trends and seasonality detection and Pruned Exact Linear Time (PELT) for change point detection. Analysis of data on the implemented Internet of Things technology in this experiment has achieved nonvisual detection of Northern Leaf Blight (NLB) disease on maize within four days post inoculation from monitored Volatile Organic Compounds and ultrasound emission