Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources. Most solutions for yield forecast rely on NDVI (Normalized Difference Vegetation Index) data, which is time-consuming to be acquired and processed. To bring scalability for yield forecast, in the present paper we describe a system that incorporates satellite-derived precipitation and soil properties datasets, seasonal climate forecasting data from physical models and other sources to produce a pre-season prediction of soybean maize yield---with no need of NDVI data. This system provides significantly useful results by the exempting the need for high-resolution remote-sensing data and allowing farmers to prepare for adverse climate influence on the crop cycle. In our studies, we forecast the soybean and maize yields for Brazil and USA, which corresponded to 44 of the world's grain production in 2016. Results show the error metrics for soybean and maize yield forecasts are comparable to similar systems that only provide yield forecast information in the first weeks to months of the crop cycle.
@cite_3: Meteorological uncertainties affect crop yield portentously during different stages of crop growing seasons, therefore several studies have been carried out to forecast crop yield using climatic parameters with empirical statistical regression equations relating regional yield with predictor variables. In this study an attempt has been made to develop Crop Yield Forecasting models to map relation between climatic data and crop yield. Present study was undertaken for forecasting rice yield by adaptive neuro fuzzy inference system (ANFIS) technique based on time series data of 27 years, yield and weather data (w.e.f. 1981-82 to 2007-08) obtained from G. B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand, India. Keywords: climate, crop yield, ANFIS, forecasting
Kumar @cite_3 performed rice yield forecasting by adaptive neuro fuzzy inference system (ANFIS) technique. For that, they used 27 years time series data of yield and weather. ANFIS is an effort to integrate the benefits of neural networks and fuzzy logic in a single framework by using linguistic information from the fuzzy logic and learning capabilities of an artificial neural network (ANN). Quantitative performance assessment for rice and wheat yield observations in India showed good applicability of the technique in yield prediction.