Field-based crop cut experiment (CCE) data is a gold standard of ground truth data for crop analytics, yet it is time-consuming and challenging to scale over large areas. Satellite remote sensing-based estimates can cover a large area, yet their accuracy is highly subject to the availability and quality of ground truth data. To address these two interlinked challenges, IFPRI, University of Twente/ITC, ICRISAT, and aWhere developed a two-stage pilot project and applied it in Odisha, India. To disseminate the results of the project, a webinar “Next-Generation Crop Production Analytics using Dynamic Area Sampling Frames and Smartphone 3D Imaging", co-presented by CGIAR Geospatial Data Community of Practice and the Enabling Crop Analytics at Scale Initiative (ECAAS). The webinar had 110 male and 21 female participants. It discussed a new two-stage crop production analytics approach piloted by leveraging cutting-edge satellite remote sensing and geo-statistical techniques to address the dual issues of inefficient ground-truth sampling design and inaccurate in-field crop yield measurement methods. First, the dynamic area sampling frame approach uses spatially detailed weather information to cue field data collection over areas of high in-season variability, analyzed using long-duration temporal Normalized difference vegetation index (NDVI) profiles. This approach can help crop analytics practitioners, such as the Ministry of Agriculture or crop insurance providers, to strategize where to collect data, when, and how many. At the field level, a smartphone-based 3D imaging technique was developed to help data collectors, including farmers themselves, expedite the collection of crop yield measurements without cutting crops. These data will be used along with photos of the crop to train a deep-learning model to estimate yield, which can then be bootstrapped for use in smartphones. It is anticipated that this two-stage approach can be adopted by other regions and production systems to improve the accuracy of crop analytics at scale.
The webinar recording is available at CGIAR Big Data YouTube Channel
The technical report is available to download from the CropAnalytics.net project website
The software is open-source and available to download from IFPRI’s GitHub account