Agricultural Informatics - Группа авторов

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      The continuous image of the field is captured by the camera in every millisecond and gets processed for any unwilling pattern from known pattern. HOG (Histogram Oriented Gradient) image processing strategy is applied here to distinguish between the known and unknown pattern. The known image pattern like crop leaf are treated as training image set which is input to the image processing algorithm. Then by image comparing technique, classifier classifies the image as known or unknown. If any unknown pattern is observed moveable then it will be treated as a weed and unknown pattern is observed not movable then it will be treated as pest.

      Figure 2.4 Proposed image processing method to detect pest and weed.

      The same image processing method is used in the integrated Agro-IoT system to detect weed and pest and finally store the data in image database. Figure 2.4 illustrates the internal image process method used in the proposed integrated Agro-IoT system.

       2.5.2 Fire Detection Process

      The researchers have proposed an image processing technique to detect the flame and detect the fire region. Figure 2.5 illustrates the flow chart of the proposed method which have used in the proposed integrated Agro-IoT system to detect the fire captured through the camera.

       2.6.1 Sensors

      The integrated Agro-IoT system uses different sensors which had been discussed in Table 2.1.

       2.6.2 Camera

      The integrated Agro-IoT system uses a night vision camera which has zooming capacity and will capture the image of the field in every millisecond. No need of having SD card inside the camera as will transfer the images directly to image databases.

      Figure 2.5 Proposed image processing method to detect fire region.

       2.6.3 Water Pump

      The water pump pumps water from the water reservoir and fill the field with water as need.

       2.6.4 Relay

      The integrated Agro-IoT system uses a relay to open or close the circuit as per the requirement for different operations. It basically acts as a switch.

       2.6.5 Water Reservoir

      The Water Reservoir stores water from different sources for watering the field when require.

       2.6.6 Solar Panel

      The integrated Agro-IoT system uses solar panel to use solar energy for running water pump, camera, beaglebone black and GSM module.

       2.6.7 GSM Module

      The integrated Agro-IoT system uses a GSM module to establish a connection between beaglebone black and the GSM–GPRS enabled mobile system.

       2.6.8 Iron Railing

      The integrated Agro-IoT system uses iron railing surrounding the total field to prevent the crops from intruders like goat, cow, etc.

       2.6.9 Beaglebone Black

      The integrated Agro-IoT system use Beaglebone Black, a small stand-alone linux computer. Here used as an embedded system. Figure 2.6 illustrates the model of beaglebone black.

      Figure 2.6 Beaglebone black.

       2.7.1 Raw Comparison

      To get Quick Overview of each.

Specification BeagleBone Black Raspberry Pi Result
Processor 1 GHz TI Sitara AM3359 ARM Cortex A8 700 MHz ARM1176JZFS BeagleBone Black Winner
RAM 512 MB DDR3L @400MHz 512 MB SDRAM @ 400MHz BeagleBone Black Winner
Storage 2 GB on-board eMMC, MicroSD SD BeagleBone Black Winner
Operating Systems Angstrom (Default), Ubuntu, Android, Arch Linux, Gentoo, Minix, RISC OS Raspbian (Default), Ubuntu, Android, Arch Linux, Fedora, RISC OS Tie
Power Draw 210–460 mA @ 5V 150–350 mA @5V Raspberry Pi Winner
GPIO Capability 65 Pins 8 Pins BeagleBone Black Winner
Peripherals 1 USB Host, 1 Mini-USB Client, 1 10/100 Mbps Ethernet 2 USB Hosts, 1 Micro-USB Power, 1 10/100 Mbps Ethernet, RPi Camera Connector Tie

       2.7.2 Ease of Setup

      Raspberry Pi bit Laborious whereas BeagleBone Black as simple as it gets.

      Winner: BeagleBone Black.

       2.7.3 Connections

BeagleBone Black Raspberry Pi

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