AI-Augmented Big Data Analytics for Precision Agriculture Yield Forecasting
DOI:
https://doi.org/10.15662/IJEETR.2023.0506021Keywords:
Key phrases of relevance, precision agriculture, yield forecasting, decomposition analysis, statistical modelling, machine learning, artificial intelligence, big data, remote sensing, crop appearance, AI-augmented solutionsAbstract
Significant investments in data collection through remotely-sensing and meteorological stations, the creation of soil health cards for Indian states, and the installation of crop health sensors now make it possible to apply Big Data and AI technologies in agriculture. Forecasts of crop yields made before harvesting of the respective crop, rather than estimates that follow the harvest, can guide import-export strategies, thereby enhancing food security. An architecture is proposed to perform AI-augmented Big Data analytics for predicting yields before harvest, using Big Data from various sources as well as statistical, machine learning, and deep learning methodologies. Once the required analytics are built and validated for a given region, the architecture supports seamless generalization to other regions—both within India and across the globe. Cross-regional generalization requires harnessing the large volume of analytics built across multiple combinations of yield-target crops, provinces, stations, and years in India.
Support for cross-regional generalization come from predictions being fed into virtually equipped ensembles, which subsequently report the winning model for predicting agronomically pertinent features of the region, namely Temperature, Evapotranspiration, Soil Moisture, Normalized Different Vegetation Index—either in combination or all together—and Product of Precipitation in Dry Spells—also either in combination or all together. The need for extensive and costly ground-truth measurements is then overcome through the widespread establishment of remotely-sensed data and other data sources for parts or for most regions of the world.
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