Abstract

Small-scale farming can benefit from the usage of information and communication technology (ICT) to improve crop and soil management and increase yield. However, in order to introduce digital farming in rural areas, related ICT solutions must be viable, seamless and easy to use, since most farmers are not acquainted with technology. With that in mind, this paper proposes an Internet of Things (IoT) sensing platform that provides information on the state of the soil and surrounding environment in terms of pH, moisture, texture, colour, air temperature, and light. This platform is coupled with computer vision to further analyze and understand soil characteristics. Moreover, the platform hardware is housed in a specifically designed robust casing to allow easy assembly, transport, and protection from the deployment environment. To achieve requirements of usability and reproducibility, the architecture of the IoT sensing platform is based on low-cost, off-the-shelf hardware and software modularity, following a do-it-yourself approach and supporting further extension. In-lab validations of the platform were carried out to finetune its components, showing the platform’s potential for application in rural areas by introducing digital farming to small-scale farmers, and help them delivering better produce and increasing income.

Highlights

  • It is evident how agriculture can benefit from the usage of information and communication technology (ICT) with farming-related processes, in the whole value chain, being optimized, resulting in better produce and increased yields, a much desired outcome to cope with the ever-increasing population and food demand

  • Sensors 2020, 20, 3511 small-scale farmers in rural Africa [5]. This platform can be broken down into three main components, namely (i) the sensing box that follows a Do-It-Yourself (DIY) approach which is based on off-the-shelf hardware and extensible software; (ii) the computer vision that comprises the software for controlling the camera and illumination system to capture soil images, and for classifying the soil type; and (iii) the protective casing that shelters all the hardware, allowing easy assembly, usage and transportation of platform

  • To ensure that the casing met the protection needs of the Internet of Things (IoT) sensing platform, we identified the main requirements that drove the design of the initial concept, namely: (i) prevent moisture and dust from reaching the hardware; (ii) robust outdoor use; (iii) compact; (iv) easy assembly; (v) based on locally available resources; (vi) easy usability; (vii) safe transport; viii) simple maintenance

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Summary

Introduction

It is evident how agriculture can benefit from the usage of information and communication technology (ICT) with farming-related processes, in the whole value chain, being optimized (e.g., from seeding to reaching the shelves), resulting in better produce and increased yields, a much desired outcome to cope with the ever-increasing population and food demand. Sensors 2020, 20, 3511 small-scale farmers in rural Africa [5] This platform can be broken down into three main components, namely (i) the sensing box that follows a Do-It-Yourself (DIY) approach which is based on off-the-shelf hardware and extensible software; (ii) the computer vision that comprises the software for controlling the camera and illumination system to capture soil images, and for classifying the soil type; and (iii) the protective casing that shelters all the hardware, allowing easy assembly, usage and transportation of platform. The proposed IoT sensing platform is part of the Project AFRICA [6] that aims at developing a green-energy driven technology solution to support the on-site, cost-affordable fertiliser production to small-scale farmers in Africa.

Background and Related Work
How ICT Enables Digital Farming
Digital Farming for Africa
Proposed IoT Sensing Platform
Architecture
Sensing Box Component
Analysed Sensors and Probes
Detailed Overview of the Sensing Box Hardware
Detailed Overview of the Sensing Box Software
Computer Vision Component
Algorithms and Datasets
Soil classification using Convolutional Neural Networks
Classes
Casing Component
Proofs of Concept
Soil Moisture
Soil pH
Ambient Temperature
Computer Vision
Prototype Validations
Findings
Conclusions and Future Work

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