DRSRS inks deal with Agrivision to Develop Platform for Crop Monitoring, Yield Forecasting
By Shadrack Nyakoe
The Directorate of Resource Surveys & Remote Sensing (DRSRS) and the Kenyan Smart Agri-Tech Company (Agrvision) have signed a 5 years strategic partnership plan in the form of a memorandum of understanding (MoU) to develop a nationwide unified platform for crop monitoring and yield forecasting powered by Satellite imagery and artificial intelligence.
The MoU was signed by Deputy Director of DRSRS Charles Situma and the Chief Operations Officer of Agrvision Oscar Mwai at the DRSRS head office in Nairobi opening a direct engagement between the two institutions on the implementation of a road map for a Nationwide crop forecasting and monitoring system in Kenya.
The agreement aims to bring the two entities’ expertise into action to develop a smart unified platform to digitally monitor and classify crops around Kenya and to provide an advanced analytics tool to enhance data-driven decisions. This will contribute to the social and economic development of the agriculture and forestry sectors in Kenya.
According to the Deputy Director of DRSRS Mr. Charles Situma, “As the official institution advising the Government of Kenya on matters related to Remote Sensing and Geographical Information systems(GIS) and gathering of data on the environment and natural resources around the country, DRSRS entered into this strategic collaboration with Agrvision to work together with an Agri- tech expert in developing the right digital tools and usage of advanced data collection and analytics technologies that can help the agricultural sector and decision-makers in the country, have full visibility and data-driven decisions that enhance food security programs to achieve better sustainable results”
While the Agricultural sector, including crops and livestock, is one of the most important sectors in the Kenyan economy with around 30% contribution to the GDP and as the main source of livelihood for the majority of Kenyan people in terms of food security, economic growth, employment creation, off-farm employment, and foreign exchange earnings. The sector is extremely vulnerable to climate change largely due to the increasing temperatures, changing rainfall patterns and extreme weather events with poor agricultural practices, low-quality inputs and lack of access to knowledge.
Serious steps and programs need to be adapted to ensure the provision of adequate food for a growing population and to increase export crops to generate foreign exchange. “Climate change is not a remote future event, it started already and the effect in the agriculture and forestry sector is high, and as part of government already running programs toward food security to decrease climate change effect in the sector, traditional ways and agricultural practices which are based on poor experiences are not sufficient.
A full transformation in the agricultural sector is needed, in which data play a major role for better, more timely and actionable knowledge is a precursor. And that’s where DRSRS found the interest to work with Agrvision in using and customizing their well-developed crop monitoring and forecasting digital tools to develop a unified platform to work as the main sources for data collection and analytics to the agriculture sector” Mr. Situma added.
With more than 15+ years of domain expertise in the Agri-tech sector globally. Agrvision developed a smart crop and forestry monitoring digital platform analyzing all big data collected through satellite imagery and specialized drones, using advanced data analytics algorithms, powered by machine learning, and artificial intelligence customized models and tools, to help farmers, forestry professionals, agricultural institutions and NGOs having useful insights and predictive analytics for a better data-driven decision making, delivered through customized convenient easy-to-use digital channels.
On his part, Agrvision Chief Operations Officer Oscar Mwai said, “We believe that agriculture should be not only a Smart but also a Sustainable industry. That’s why we’re committed to simplifying the remote sensing-based precision agriculture technology, making it universally accessible and practical, by using cutting-edge Ai/ML models and algorithms to analyze big Agri-data that is collected and provide highly-precise information about fields, crops and forests, participating in the agricultural transformation journey with government institutions and decision-makers for creation of more sustainable food security programs.”
With this strategic collaboration and long term partnership with DRSRS, Agrvision’s mission is to work closely with the DRSRS team to develop and customize the unified platform with an interactive digital map classifying all crops around the country powered by Ai with field boundaries and have real-time analytics for crop monitoring and rotation, weather forecast and soil moisture to be collected from high-resolution satellite imagery and specialized drones to be able to provide predictive analytics and yield forecasts.
“Our Crop and forestry monitoring platform, collect data from different sources like satellite and engage it with advanced analytics for vegetation indices, water stress, soil moisture, temperature effect and weather forecast to provide the end-user with useful descriptive and predictive insights. These insights are visual, simple-to-understand pieces of information that can be used to guide action that makes working in agriculture and forestry easier, and saves precious time and resources, we believe that this collaboration with DRSRS with the in-depth knowledge they have on the agricultural mapping and remote sensing, will add strength to the development and customization process for having the digital platform up and running “Mr. Mwai Added.
As part of this MoU, both entities appointed the project team to work closely together on drafting the final project scope of work to build the project strategy and road map for the platform development, with plans to start directly running the pilot project to analyze the needed final features to be embedded in the model.