Abstract
This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July–August (JA) database as the best months to predict corn and soybean yields. Six different AI models for crop yield prediction are tested in this research. Then, a comprehensive and objective comparison is conducted between the AI models. Particularly for the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio. In terms of mean absolute error (MAE), our DNN model with the JA database was approximately 21–33% and 17–22% more accurate for corn and soybean yields, respectively, than the other five AI models. This indicates that corn and soybean yields for a given year can be forecasted in advance, at the beginning of September, approximately a month or more ahead of harvesting time. A combination of the optimized DNN model and spatial statistical methods should be investigated in future work, to mitigate partly clustered errors in some regions.
Highlights
Accurate estimations of crop yields are important for many agronomic issues, including agricultural management, national food policies, and international crop trade
To analyze the effects of phenology, we derived 13 cases using different combinations of months, including one case representative of the entire growing season (GS) between May and September, five cases corresponding to individual months, four cases corresponding to two successive months, and three cases corresponding to three successive months (Tables 4 and 5)
For the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio, to improve the crop yield prediction accuracy
Summary
Accurate estimations of crop yields are important for many agronomic issues, including agricultural management, national food policies, and international crop trade. For this reason, a variety of methods are employed for crop yield prediction, and the application of satellite images is becoming increasingly important. Satellite remote sensing techniques, which continuously cover large areas, can help provide more accurate estimations of crop yields. In addition to vegetation indices, various land surface variables, such as weather elements, soil moisture (SM), hydrological conditions, soil properties, and fertilizer application, have been used in crop yield estimation [8,9]. Awad [10,11] presented a new mathematical optimization model to compensate for the lack of high resolution remote sensing images, and estimated potato yield using the biomass calculated by the model
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