IOT Architecture for Real Time Maize Stem Borers’ Detection and Capturing in Precision Farming

Abstract

The emergence of Information and Communication Technology (ICT) has brought tremendous change in Precision Farming. The
invention of these technologies enhanced farming techniques for optimal yield of crops and its management in the agricultural
sector. These technologies aid farmers to survey, monitor and collect data remotely from the farm and communicate the same
anywhere in the world. For crops in the farm to grow healthy, they need caring and monitoring from insects’ attack. Farmers
adopt many measures to drastically reduce the influx of these insect pests in the farm. However, the measures put in by farmers to
carry out monitoring are still not enough because monitoring requires the presence of the farmers in the farm and scouting for the
insects’ presence during the developing stages of the crops. It was recently announced of the aggressive insect pests (stem borers)
that attacked maize crops and caused poor yield of the maize in that season. The rate at which these insect pests (Spodoptera
species) migrate in numbers and eat the leaf, ear and stem of maize within few days is alarming. This work presents IoT
architecture for real time maize stem borers’ detection and capturing in precision farming. This system would monitor for the
presence of these insect pests in the farm (e-scouting) and communicate to the farmers remotely thereby relieving them visiting
their farms frequently. The IoT system hardware has two modules. The first module is the slave device that consists of the
camera, motion sensor, super light LED, battery, GPS, antenna, etc. whereas the second module, the master device, comprises of
the power supply, Jetson Nano, 4G wireless router, Wi-Fi dongle, SD card, etc. Both modules were designed and integrated for
use in detecting and capturing the targeted maize insect pests in the farm. The slave devices would be installed in the farm at
different coordinates and linked to the master via Wi-Fi technology and antenna. The master device accepts the detected insect
pest data, processes and recognizes the insect through machine learning algorithm/trained model and finally sends to dedicated
cloud storage for further use by farmers. The prototype IoT system was successfully tested at University of Port Harcourt’s Farm
for Teaching and Research. This hardware would greatly aid decision making by farmers in ensuring that these maize stem borers
are recognized and controlled in precision farming.