Helmets Labeling Crops: Innovative Approach Revolutionizing Rapid Crop Label Data Collection in East and Southern Africa
Posted on: 13 Apr, 2023
As technology continues to evolve, new opportunities arise for innovative solutions in various fields, including agriculture. In recent years, advancements in earth observation data, cloud computing, and machine learning capabilities have played a central role in agriculture. These technologies are being used to monitor crops, inform policies, and take anticipatory actions geared towards achieving sustainable goals. In light of this, various innovative initiative solutions and approaches that leverage technology to improve agriculture have been developed.
One such initiative is the "Helmets Labeling Crops" project, implemented by the Regional Centre for Mapping of Resources for Development (RCMRD), the NASA Harvest Africa Program based at the University of Maryland, and the IGAD Climate Prediction & Applications Centre (ICPAC) in partnership with other organizations and funded through the Lacuna Fund. The project is designed to collect rapid data on the agricultural landscape using GoPro cameras mounted on motorbike helmets and vehicles. The data collected through this initiative aims to strengthen the availability of field crop labels/data.
The project was implemented in four African countries, namely Kenya, Tanzania, Uganda, and Zambia. These countries were chosen because they are among Africa's top food-producing nations, and the agriculture sector is a major contributor to their economies. In these countries, RCMRD and ICPAC worked closely with respective ministries of agriculture to ensure the success of the project and sustainability.
The project involved training a team of field officers in each country to capture images of crops using the GoPro cameras mounted on their helmets and vehicles. More than 100 field officers were successfully trained in the operation of the equipment and in the proper capturing of images of the crops. The data collected through this initiative is expected to produce unprecedented, machine-learning-ready crop labeled datasets which will be useful in training machine learning models for cropland and crop type mapping.
The development of cropland and crop type maps is a crucial step towards improving crop monitoring and management as some countries still use outdated maps. These maps will serve as a valuable input to the National Crop Monitor initiatives, which aim to enhance the monitoring of crop conditions during the growing season. By providing accurate and up-to-date information on the location and type of crops being cultivated, the maps will enable policymakers to make informed decisions regarding crop management. Furthermore, the cropland and crop type maps will also be a critical input to the Regional Food Balance Sheet (RFBS), which is being co-implemented with the Food and Agriculture Organization (FAO). The RFBS is a valuable tool that helps to assess the availability of food in a particular region, which is essential for ensuring food security. By providing accurate and comprehensive data on the amount and type of crops being produced, the cropland and crop type maps will enable the RFBS to make more precise estimates of food availability and requirements in the region.
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