Candidate: Mohita Chaudhary
Title: Yield and Price Forecasting for Fresh Produce using Deep Learning Models
Date: July 2, 2021
Time: 2:00 PM
Place: Remote
Supervisor(s): Karray, Fakhri
Abstract:
It is important to have a accurate estimate of the yields and prices of fresh produce (FP) for maintaining an e ective Fresh Produce Supply Chain Management (FSCM). Since, the FP comprises of the perishable goods, it is cumbersome to manage and keep a track of logistics, which makes it important to have an estimate of the FP yield to have a better management of the supply and demand. In addition, having a reliable estimate of the FP prices helps the food company to bid the right price to the wholesalers. This prevents the food company from bidding unreasonable price and incurring any loss. Computational tools for forecasting yields and prices for fresh produce have been based on conventional machine learning approaches or time series modeling. These approaches can neither e ectively capture the complex relationships between the inputs and the outputs to the models nor can they handle large datasets. To overcome such drawbacks, Deep Learning (DL) based approaches are proposed in this work for forecasting the yields and prices of FP. Soil and weather parameters of counties across California are used to forecast the yields and prices of FP like berries and apples.
We investigate the most e
ective input parameters for forecasting strawberry yields and prices. The set of parameters used for this investigation are soil parameters alone and soil parameters along with the weather parameters. For this forecasting, the ensemble of two DL models is used namely, Convolutional Neural Networks and Long Short Term Memory with Attention (Att-CNN-LSTM) and Convolutional LSTM with Attention (Att-ConvLSTM). It is found that using soil and weather parameters together gives better forecasting results than using soil or weather parameters alone. Also, various compound DL models like Att-CNN-LSTM, Att-ConvLSTM, Temporal Convolutional Network (TCN) and SeriesNet with Gated Recurrent Unit (SeriesNet-GRU) are tested for forecasting, to determine the best performing DL model.
It is found that the ensemble of two compound DL models Att-CNN-LSTM and SeriesNet-GRU gives the best forecasting results with an improvement of around 7% in the value of Aggregated Measure (AGM) than the component compound DL models. It also outperforms the previous work done in literature with an improvement of around 14% in the value of AGM.
To generalize the
ndings, transfer learning technique is utilized amongst the yield fore- casting models of the similar crops. To overcome the computational complexity of retraining DL yield forecasting models for each type of FP, it is necessary to have a generalization of the models' application to similar FP with minimal retraining. Two berries are considered in this work, California strawberries and raspberries which have similar yield, since the two follow similar time series on the basis of a number of parameters such as lag, seasonality and trend.
The voting regressor ensemble of two compound DL models Att-CNN-LSTM and SeriesNet with GRU is used. It is found that the transfer learning gives comparable results to training from scratch and reduces the processing time by half.