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

•Maize plants emits an increased pattern of Volatile Organic Compounds and ultrasound when inoculated with Exserohilum turcicum fungus.•Internet of Things (IoT) sensors and artificial intelligence models are able to capture and profile nonvisual signs of Northern Leaf Blight maize disease.•Statsmode...

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Veröffentlicht in:Smart agricultural technology 2023-10, Vol.5, p.100260, Article 100260
Hauptverfasser: Maginga, Theofrida J., Bakunzibake, Pierre, Masabo, Emmanuel, Massawe, Deogracious P., Agbedanu, Promise R., Nsenga, Jimmy
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Sprache:eng
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Zusammenfassung:•Maize plants emits an increased pattern of Volatile Organic Compounds and ultrasound when inoculated with Exserohilum turcicum fungus.•Internet of Things (IoT) sensors and artificial intelligence models are able to capture and profile nonvisual signs of Northern Leaf Blight maize disease.•Statsmodel for trends and seasonality detection and Pruned Exact Linear Time (PELT) are useful models for change point detection on timeseries data.•Nonvisual detection of Northern Leaf Blight (NLB) disease on maize can be detected within four days post inoculation from monitored Volatile Organic Compounds and ultrasound emission. 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.
ISSN:2772-3755
2772-3755
DOI:10.1016/j.atech.2023.100260