Predicting Crop Yields
Accurate crop yield prediction has significant implications for people, businesses, and countries everywhere - mistakes can impact food security and magnify effects from climate change. Advances in technology have taken us a long way away from consulting the Farmers Almanac to guide planting or harvesting decisions. Modern farms now utilize a wide range of information and technology, including soil sensors, hyper localized weather and precipitation data, GPS tracking, and satellite imagery to assist in virtually every aspect of the agricultural process.
The availability of large amounts of detailed agricultural, weather, harvest, and associated data, along with advances in data science and computing power, have made predicting crop yields much easier, accurate, and more reliable. This, in turn, has allowed farmers and government officials the ability to better evaluate needs and plan resources more efficiently.
Automated machine learning (AutoML) is particularly well suited to the task of analyzing the large and disparate datasets inherent to agriculture. Auger.AI’s AutoML tool greatly reduces the time spent selecting algorithms and tuning hyperparameters by automating the process and trying every available algorithm and hyperparameter combination. This process generates a leaderboard showing the best combinations from which an ensemble is created, combining the best attributes of the leading algorithms and hyperparameter options. Additionally, Auger employs a “warm start” process to algorithm/hyperparameter selection, generating more accurate results in less time.
All this means more more accurate harvest forecasting, better use of resources, less impact to the environment, and increased food security.
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