Thursday, 13 September 2012

Automating Crop Surveillance – theory vs. praxis - part 1/3



Why?

An annual crop survey is carried out in Uganda every year by the National Crop Resources Research Institute (NaCRRI) targeting the cassava plant. Normally experts are required to go out to different gardens in the four disparate regions of Uganda and fill out paper forms indicating the incidence and severity of diseases affecting cassava by visually examining cassava plants.

Automating this process is what we set out to do – the main reason being to enable policy makers access this data immediately as it is collected in the field, and secondly to make several component processes of the survey more efficient for example automated diagnosis and severity scoring, automated count of disease vectors on the cassava leaves and uniform scoring of necrotized cassava tubers.


Theoretical Solution

In brief…

Replace the paper forms with cheap mobile phones running Android OS and using the existing telecommunications network get geo-tagged surveys directly on to a map in real time from data collectors in the field.

Because of the available processing power on the phones, automate the cumbersome tasks of whitefly count and severity scoring on the phone so experts need not be the ones to carry out the survey and can use their limited time doing something else.


Component-wise

We built a system based on the Open Data Kit (ODK), Google AppEngine, the Google Map API and Google Fusion Tables – heck we even communicated using gmail ☺

ODK: We used ODK-build to build forms usable on the mobile devices (converting the paper form to an appropriate mobile format). ODK-Collect was used to collect the data on the mobile devices. ODK is presently only compatible with devices running Android OS.
Google AppEngine (GAE): Used GAE to host the server side of the system that receives the uploaded data from the mobile phones. It was implemented as an instance of ODK-Aggregate.

Google Map API: We integrated the API with GAE to display the uploaded data on a dynamic map.
Fusion Tables:
Used to seamlessly interface data in ODK Aggregate with the Map API.
A real-time map of the crop survey

2 comments:

  1. Hmm it is really important for the farmers to see this...

    ReplyDelete
  2. will be interesting to know about your tool usability evaluation

    ReplyDelete