Farming Portfolio Optimization with Cascaded and Stacked Neural Models Incorporating Probabilistic Knowledge for a Defined Timeframe

摘要

Optimizing the allocation of farmland between different crops is provided. First and second Deep Boltzmann machines (DBMs) are built, wherein the hidden layers of the DBMs are split into a plurality of neural networks, each neural network modeling a different timeframe of crop growth. A plurality of factors related to crop growth are fed into the first DBM, which is trained to produce a first multi-class output of predicted maximum crop yields within a specified overall timeframe. The first multi-class output is fed into the second DBM, which is trained to produce a second multi-class output of predicted crop yields. The second multi-class output is fed into a decision support system that generates a recommended allocation of the farmland among different crops during different timeframes to maximize total yield.