“So, you want to be a smart farmer”

Why smart farming?

Source: WorldBank

The agriculture sector plays an important role in generating employment and contribution to the gross domestic product (GDP) globally and on regional- and country- levels. 27% or approx. 1 in every four people in the world work in agriculture, as per WorldBank. Or we look at the regional data, almost one in every two people (53%) in Sub-Saharan Africa works in the agriculture industry. Agriculture, forestry, and fishing contributes 3.4 percent of World GDP in 2018, while in Sub-Saharan Africa, the contribution reached 16% of GDP.

Farmers in the developed world are increasingly using sensors to figure out what, when and where to grow it to improve the agriculture output. But agriculture still remains underdeveloped in Africa in terms of the amount of potentially arable land and post-harvest losses as in many cases farmers use traditional farming methods and techniques. So, when it was time to decide on the project for the , I decided to study IoT sensors in agriculture.

How can you do smart farming?

The legend goes as follows, historically, a farmer, an owner of several plots has been measuring the humidity level and temperature manually. Apart from having done this manual effort in visiting the fields to monitor the crop, he also has a risk of overwatering his plants, thus, getting fungus developed. He approached the IT company, requesting to set up sensors and a system that automatically monitors changes of temperature and humidity and alerts him regarding the parameter changes via an SMS, so that he can take corrective actions on time (a primary objective of the project). As a complimentary service, the IT company offers the portal which displays the collected data from different sites to perform further analysis and monitor the soil condition over time (secondary objective).

Functional requirements can be summarized as follows: 1. as the data is streamed continuously, the architecture should be fit for continuous data ingestion and data processing.

Source: Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, USA

“Data is to be collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE uses wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations. At its maximum, the network is to contain over 200+ wireless sensor installations (nodes), with a range of 6 to 27 nodes per site.

The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) are to be installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors are to be installed at 5 cm depth at six of the sites.” (source: ).

2. Some validation should be done to review the incoming data in terms of its completeness i.e. the number of data points missing per site. And in case if the data gets missing, an SMS should be sent.

3. A system should also be able to send the SMS when a temperature/humidity level is below/above the threshold level, identified by an agronomist.

4. The dashboard should show row metrics coming from the sensors such as temperature, humidity sliced by the site, and individual sensor levels.

5. Indirect metrics such as a rolling window average temperature and humidity levels should be calculated and sliced by the site and individual sensor levels.

6. Outliers/anomalies should be calculated and sliced by site- and individual sensor- levels.

7. The dashboard should display a map with the average temperature and humidity levels plotted against the overall yield data.

To be continued…

P.S. In the next series of articles I will be sharing my reading summary on the topic in general as well as my understanding of the data lifecycle in the IoT industry. I would say that the articles would be written from the newcomer perspective directed towards newcomers, so, please feel free to correct in case if some parts have little/no sense.

I’m a data consultant, specializing in ETL, reporting, BI, dashboarding and analytics