A powerful Trajectory Ahead
It is now a fact that the potential of insect-protein, which is a growth industry in Africa, especially in Kenya, can be realized by leveraging affordable and durable sensor technology that can provide insect-farmers with the real-time data needed to immediately respond and optimize the growth conditions during the fast life-cycle of target beneficial insects like the black soldier fly. However, in order to make sure that the IoT sensor technology is suitably adapted to the needs of small-scale BSF farmers, Sprint 3 involved working with a small-scale BSF farm, Zihanga Ltd, to install and monitor six IoT sensors at the farm. Zihanga Ltd was incubated by Icipe (International Centre of Insect Physiology and Ecology) and started its operations in the year 2000 with a BSF starter pack from Icipe.
Working with Zihanga Ltd came after numerous design improvements had been done both in sprint 1 and sprint 2 to ensure that the sensors were viable, seamless and easy to use in a small-scale application since most small-scale farmers are not acquainted with technology. The sensors deployed at Zihanga Ltd were used to monitor Ammonia levels in wet larvae grow house, light intensity, substrate temperature, moisture content, carbon dioxide, Nitrogen, Phosphorus and Potassium levels in the substrate. Though monitoring of these parameters is still going on, initial results show that to achieve the complete requirements of usability and practical operability at the small-scale level, the sensors should have plug and play capabilities to help small-scale farmers avoid the hassle of connection and configuration. Plug and play sensors are easy to install and use as you simply plug into a socket thereby eliminating the time and challenges associated with sensor configuration. Other benefits of plug and play sensors include: automated system set-up, less downtime for sensor repair and replacement and better diagnostics. Additionally, the architecture of the IoT sensors should be based on low-cost, off-the-shelf hardware and should have low operating power.
According to Nicholas Ndekei, the Chief Executive Officer of Zihanga Ltd, “configuration of the IoT sensors took a significant amount of time and without the technical support from icipe and Sanergy, which came as part of the practical training in the use of Iot sensors, we wouldn’t have been able to do it on our own as Zihanga Ltd.” For the whole period of Sprint 3, Zihanga Ltd staff received various training on configuration and monitoring of the sensors, data collection using ODK toolkit and data analysis. At the close of Sprint 3, Nicholas Ndekei said that even though they had learnt how to configure and monitor the Iot sensors as Zihanga Ltd, quick replication of the sensors among other small-scale BSF farmers in Africa would require a plug and play model.
Fun facts
According to recent estimates, there are approximately 13.8 billion IoT devices in the world today, outnumbering humans by about six billion, give or take. Over the past decade, connected farming has become an industry mainstay. By some estimates, 10% to 15% of farmers use IoT technologies on the farm across 3.1 billion acres and 250,000 farms, according to data from Alpha Brown. The Internet of Things (IoT) ecosystem, comprising of sensors, connectivity, data analytics, workflow automation platforms and applications lies at the heart of a new way of thinking that offers enormous promise to the continent of Africa in terms of addressing the challenges of food security and waste management. Regular IoT sensor technology is too expensive for ordinary small-holder farmers and insect producers in Africa but this has been mitigated by making cost a key driver of the project in Sprint 3 and by working with Zihanga Ltd, a small-scale BSF farm,to deploy and monitor low-cost IoT sensor prototypes. Sprint 4 will involve continuing to work with Zihanga Ltd to establish an ideal price point for the IoT sensor technology.
Why compare when no two people are alike?
With Sanergy providing credibility of the IoT sensor technology as a reference implementer, Sprint 3 also involved Sanergy comparing two sets of different sensors from Wisense and another manufacturer. Multiple sensor nodes from Wisense and the second manufacturer were fabricated and deployed to incorporate the following in one node: substrate moisture content, substrate temperature and ambient conditions (Ammonia, Carbon dioxide, Relative humidity, Temperature, Light Intensity, and Airflow). Results from Wisense sensors showed that more sensors in one node required a large battery capacity as the frequency of battery replacement was every 2 days. Low battery power was not a problem with the second set of sensors from the second manufacturer as they were fitted with rechargeable batteries that provided power for an extended period of time. However, the design of the second set of sensors was not up to standard with respect to hardware assembly when compared to the Wisense design. This was mostly so because the second set of sensors’ design had cable entry points that were not properly sealed. This made one node to have a short circuit. As the project is almost coming to an end, a key learning from this comparison experiment was that it is useful to test competing products early on in the project so that any design improvements, informed by comparison experiment, on the product of choice can be tested and implemented early enough in order to take full advantage of those improvements in the subsequent phases of the project.
Thus, even though the Wisense sensors were assembled in a very basic way without robust hardware integration, The sensor prototypes from the second manufacturer did not provide enough proof for superior adaptability, flexibility and reliability as compared to WiSense sensors. This therefore means that for the subsequent sprints, only Wisense sensors will be used. The basic architecture of the Wisense design with sensor probes that are easily detachable via connectors for easier replacement makes them suitable for application at the small-scale level.
Sensor calibration
In Sprint 3, Sanergy also proved that calibration of the sensors via the dashboard is possible. Sensor calibration is an adjustment or set of adjustments performed on a sensor to make the sensor function as accurately, or error free, as possible. Connecting the sensors to a dashboard enables the calibration and recalibration of all the connected sensors from the convenience of a single application . A dashboard will thus make it possible for a BSF farmer to remotely, easily and quickly calibrate all their connected sensors in one go. Calibration of the sensors helps to avoid the following errors in measurement:
- Incorrect Zero Reference Error — Incorrect zero reference error occurs when a measuring instrument indicates a value even when there is nothing being measured. sensors may not have a proper zero reference as they are electronic devices and the reference voltage or signal may drift over time due to changes in ambient conditions.
- Error due to shift in sensor’s range — the sensors’ range may shift due to the same conditions mentioned above and/or when the operating range of the process is changed
- Error due to Mechanical Wear or Damage — Mechanical wear or damage of a sensor may result in an error in measurement. This kind of error necessitates sensor repair or replacement.
Leveraging Odoo Enterprise Resource Planning (ERP)
ERP is a software system that helps to run an entire business, supporting automation and processes in all aspects/areas of the business e.g. production, human resources, procurement, finance and more. In order to properly create and leverage the IoT sensor technology, it was critical for Sanergy to have a proper ERP system in place — essentially, this is why Sanergy fast-tracked its transition to Odoo ERP. Sanergy implemented all sensor dashboards on Odoo in Sprint 3, thus centralizing data into one, easy-to-use system. With multiple user access to the Odoo dashboard made possible through a common link, the system seamlessly ties all the Sanergy business applications together into a consolidated platform and allows for data sharing in real-time from all kinds of external machines and devices. The continuous stream of data from the IoT sensors allowed Sanergy to perform real-time analysis, helping us to gain actionable insights to improve and optimize BSF production. This was made even more possible through the testing and eventual implementation of graphical and data export functionalities. Other critical user functions on the dashboards which include linking data profiles to work orders / batch numbers, pushing of alerts and user adjustable set points will form a key part of Sprint 4 experiments. But more importantly, Sprint 4 will have a big bias for external data access by Zihanga Ltd. this will position Zihanga Ltd for faster growth through real-time business insights, improved forecasting and greater operational efficiency.
If you run into a wall, don’t turn around and give up
Data analytics is at the core of every smart agriculture solution that leverages IoT sensor technology. This was especially evident in Sprint 3 when six sensors were deployed at Zihanga Ltd. Despite training in sensor monitoring, data collection and analysis, Zihanga Ltd couldn’t make a direct link between the data they saw and the actions they could take to optimize production. This was so because Zihanga Ltd questioned their own ability to understand what the data meant especially in instances where data analytics revealed new insights that were contrary to Zihanga Ltd’s experience. This prompted icipe and Sanergy to provide continuous training to Zihanga Ltd on data analytics and interpretation. By the end of Sprint 3, and as a result of the continuous training, Zihanga Ltd had built powerful data analytics capabilities and were also able to explicitly link data to actions. At the end of the last session of the data analytics and interpretation training, Nicholas Ndekei. Zihanga Ltd’s CEO had this to say, “Having the IoT sensors at Zihanga Ltd has given us the ability to foresee the output of our production which allows us to plan for better product distribution. If we know exactly how much BSF we will produce, we can make sure that the product won’t lie around unsold.”
Power demand
Sensor nodes are usually battery powered, so as sensor networks increase in size and number, the frequency of battery replacement becomes high and time consuming. Moreover, a battery that is large enough to last the life of a sensor node would dominate the overall size of the node, and thus would not be attractive or practical. Low battery power has been a big challenge in the project right from Sprint 1. Essentially, this is why Sanergy did experiments with both rechargeable and replaceable batteries in Sprint 3 where rechargeable batteries proved superior as they provided power for an extended period of time and were less expensive. In sprint 4, Wisense sensors will be fitted with rechargeable batteries. Additionally, in Sprint 4, design optimization/improvements for critical sensors will be done to reduce power demand.
What can we conclude?
That with shrinking agricultural lands, exponential growth of world population and depletion of finite natural resources, the need to sustainably enhance farm yield has become more critical than ever. BSF farming is increasingly getting attractive to meet the need for sustainable feed and food production, as BSF represents an abundant feed source that can be produced using organic waste streams, therefore enhancing sustainability and circularity while building food security. It is therefore important that in order to ramp up BSF production by leveraging IoT sensor technology and meet the above demands, the technology should be low cost, low power and should be continuously adapted to reliably measure and transmit data on parameters critical to insect growth, both at large and small scale levels.
