IoT Sensing Platform as a Driver for Digital Farming in Rural Africa
- Sensing box based on low-cost, off-the-shelf hardware and software modularity, following a DIY approach for further extension without requirements of extended hardware and software development knowledge.
- Soil classifier based on computer vision with soil images acquired by a dedicated camera and controlled light system.
- Standardized 3D-printed protective casing to safeguard all hardware components from the extreme environment conditions, and focused on easy production, assembly and simple integration of the proposed solution.
- In-lab validation of each component of IoT sensing platform, namely the sensing box, the computer vision, and the casing, targeting further finetuning and improvements to allow a single, integrated platform robust enough for the target application scenarios.
2. Background and Related Work
2.1. How ICT Enables Digital Farming
- Type of farming: whether the solutions target rearing of animals or crop cultivation;
- Purpose of applied technology: focusing on the main goal of the employed solution in the farming processes, that is, monitoring, actuation, control, and automation;
- Expected outcomes: aiming at better yields, pest and disease control, quality standards, safe transportation, improved storage, nutrient levels, health status, sustainable process.
- Considered technology: referring to the employed ICT concepts to derive the solutions, that is, sensing, communication networks, computer vision, cloud infrastructure, mobile applications, AI, ML, unmanned vehicles and robots, and so on.
2.2. Digital Farming for Africa
3. Proposed IoT Sensing Platform
3.2. Sensing Box Component
3.2.1. Analysed Sensors and Probes
- LaMotte —offers a set of soil testing kits and instrumentation equipment. The testing kits are reagent-based products, offering a visual colour matching system to monitor the soil status. This is a disposable solution only possible to integrate in the sensing box by an automatic analysis of the colour matching system. The instrumentation equipment is composed by a unique system that incorporates the sensing probe and a display to show the measured values. They do not provide any communication interface, so its integration in the sensing box is not possible due to associated complexity.
- pH Sensor by Mettler Toledo —offers laboratory grade pH sensor. Some of the pH probes—as for example the pH electrode InLab Solids  and pH electrode InLab Solids Go-ISM —are designed to be used in semi solid material, so they could be used to access obtain the pH status of the soil. They provide an analogue interface over multiple physical interfaces, so they can be integrated in the sensing box due to simplicity.
- DIY sensors—low-cost sensors with low precision and robustness levels. They can be used in budget-constrained implementations, where the sensor precision and robustness are not mandatory requirements;
- Commercial grade sensors—sensors sold for commercial applications. More expensive than the DIY sensors, but more reliable and robust;
- Soil analysis kits—reagent-based products that offer a visual colour matching system to monitor the soil status.
3.2.2. Detailed Overview of the Sensing Box Hardware
- DFRobot SEN0249 analogue spear tip pH sensor . This is a pH sensor for semisolid material with an analogue interface. It is connected to the Arduino through the screw connectors on the Arduino header.
- Sentek drill & drop soil moisture, salinity and temperature probe . This is a multi-depth industrial grade soil monitoring probe that communicates through SDI. It is connected to the sensing box through the SDI interface present in the screw connectors of the Arduino header. If cost or market availability may be an issue, a low-cost alternative to measure the soil moisture and temperature is also being explored with Seeed Studio 314010012 moisture and temperature sensor . This sensor is connected to the sensing box through I2C interface present in the screw connectors of the Arduino header.
- Adafruit 2652 BME280 I2C/SPI temperature sensor . It is worth mentioning that the Adafruit BME280 also provides humidity, barometric pressure, and altitude. This sensor is connected to the sensing box by soldering its pins into the protoboard space of the Arduino header.
- Seeed Studio (TSL2561) 101020030 digital light sensor . This light sensor follows a DIY approach. It is connected to the sensing box through the I2C interface available in the screw connectors of the Arduino header.
3.2.3. Detailed Overview of the Sensing Box Software
- A Configuration Socket dedicated to communicating configurations for the software modules. In this socket, it is published the configuration for each sensor (e.g., period and duration of readings). The software modules running on Raspberry Pi subscribe to this socket to receive this information and configure themselves according to the configuration received.
- A Sensor Data Socket dedicated to communicating the data acquired from the sensors. The software modules running on the Raspberry Pi publish in this socket the readings they receive from the sensors.
- The communication on these sockets follows a publish-subscribe paradigm, where the software running on the UE opens the Configuration Socket in publisher mode and publishes the configuration message, while the software modules in the Raspberry Pi open it in subscriber mode and consumes the configuration message.
3.3. Computer Vision Component
3.3.1. Algorithms and Datasets
3.3.2. Soil classification using Convolutional Neural Networks
- Soils with strong human influence;
- Soils with limitations to root growth;
- Soils distinguished by Fe/Al chemistry;
- Pronounced accumulation of organic matter in the mineral topsoil;
- Accumulation of moderately soluble salts or non-saline substances;
- Soils with clay-enriched subsoil;
- Soils with little or no profile differentiation.
3.4. Casing Component
4. Proofs of Concept, Prototypes and Validations
4.1. Proofs of Concept
4.1.1. Soil Moisture
4.1.2. Soil pH
4.1.3. Ambient Temperature
4.1.4. Computer Vision
- Testing a set of image acquisition equipment such as the one presented in Figure 13. The controlled image acquisition setup tries to avoid light leakage and improve image quality of the soil images acquired.
- Evaluating contrast enhancement and thresholding techniques.
- Testing feature extraction in different colour spaces - the images accessed in the RGB colour space can be transformed in different colour spaces, and some computations performed, including the statistical, frequency and spatial frequency.
- These were initially considered using OpenCV, but later dropped due to the machine learning approach adopted.
4.2. Prototype Validations
5. Conclusions and Future Work
Conflicts of Interest
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Oliveira-Jr, A.; Resende, C.; Pereira, A.; Madureira, P.; Gonçalves, J.; Moutinho, R.; Soares, F.; Moreira, W. IoT Sensing Platform as a Driver for Digital Farming in Rural Africa. Sensors 2020, 20, 3511. https://doi.org/10.3390/s20123511
Oliveira-Jr A, Resende C, Pereira A, Madureira P, Gonçalves J, Moutinho R, Soares F, Moreira W. IoT Sensing Platform as a Driver for Digital Farming in Rural Africa. Sensors. 2020; 20(12):3511. https://doi.org/10.3390/s20123511Chicago/Turabian Style
Oliveira-Jr, Antonio, Carlos Resende, André Pereira, Pedro Madureira, João Gonçalves, Ruben Moutinho, Filipe Soares, and Waldir Moreira. 2020. "IoT Sensing Platform as a Driver for Digital Farming in Rural Africa" Sensors 20, no. 12: 3511. https://doi.org/10.3390/s20123511