Forecasting maturity of green peas: An application of neural networks

摘要

Maturity index (MI) is a key determinant of pea softness and ultimately retail value. Pea seed development goes through the optimal market stage for human consumption about a week before harvest. MI increases rapidly during the last 3-4 days prior to the optimal harvest which is when there is a need for better forecasting capability. Extensive field sampling is currently used to track MI in each of the individual paddocks, though it has limited ability to predict MI more than a day ahead. We developed an Artificial Neural Network (ANN) model that complements field sampling by forecasting the MI trend several days ahead. It was built using historical harvest information along with weather and climate forecasts. We implement and evaluate the ANN in a large pea growing region in Tasmania, Australia, and this paper highlights key results. The ANN produced an average error of 31.8 MI units when forecasting MI at harvest with a 7-day lead time versus the current manual method which produced an average error of 36.6 MI units for a lead time of 2 days. This means the model provides the ability to not only harvest peas closer to their ideal MI but also plan harvesting and transport logistics with a much greater lead time.