Machine Learning based Models for Fresh Produce Yield and Price Forecasting for Strawberry Fruit

Building market price forecasting models of Fresh Produce (FP) is crucial to protect retailers and consumers from highly priced FP. However, the task of forecasting FP prices is highly complex due to the very short shelf life of FP, inability to store for long term and external factors like weather and climate change. This forecasting problem has been traditionally modelled as a time series problem. Models for grain yield forecasting and other non-agricultural prices forecasting are common. However, forecasting of FP prices is recent and has not been fully explored. In this thesis, the forecasting models built to fill this void are solely machine learning based which is also a novelty. The growth and success of deep learning, a type of machine learning algorithm, has largely been attributed to the availability of big data and high end computational power. In this thesis, work is done on building several machine learning models (both conventional and deep learning based) to predict future yield and prices of FP (price forecast of strawberries are said to be more difficult than other FP and hence is used here as the main product). The data used in building these prediction models comprises of California weather data, California strawberry yield, California strawberry farm-gate prices and a retailer purchase price data. A comparison of the various prediction models is done based on a new aggregated error measure (AGM) proposed in this thesis which combines mean absolute error, mean squared error and R^2 coefficient of determination. The best two models are found to be an Attention CNN-LSTM (AC-LSTM) and an Attention ConvLSTM (ACV-LSTM). Different stacking ensemble techniques such as voting regressor and stacking with Support vector Regression (SVR) are then utilized to come up with the best prediction. The experiment results show that across the various examined applications, the proposed model which is a stacking ensemble of the AC-LSTM and ACV-LSTM using a linear SVR is the best performing based on the proposed aggregated error measure. To show the robustness of the proposed model, it was used also tested for predicting WTI and Brent crude oil prices and the results proved consistent with that of the FP price prediction

Effect of curing conditions and harvesting stage of maturity on Ethiopian onion bulb drying properties

The study was conducted to investigate the impact of curing conditions and harvesting stageson the drying quality of onion bulbs. The onion bulbs (Bombay Red cultivar) were harvested at three harvesting stages (early, optimum, and late maturity) and cured at three different temperatures (30, 40 and 50 oC) and relative humidity (30, 50 and 70%). The results revealed that curing temperature, RH, and maturity stage had significant effects on all measuredattributesexcept total soluble solids

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media

Deep Learning Based Approaches for Imputation of Time Series Models

Market price forecasting models for Fresh Produce (FP) are crucial to protect retailers and consumers from highly priced FP. However, utilizing the data for forecasting is obstructed by the occurrence of missing values. Therefore, it is imperative to develop models to determine the value for those missing instances thereby enabling effective forecasting. Usually this problem is tackled with conventional methods that introduce bias into the system which in turn results in unreliable forecasting results. Therefore, in this thesis, numerous imputation models are developed alongside a framework enabling the user to impute any time series data with the optimal models. This thesis also develops novel forecasting models which are used as a gauging mechanism for each tested imputation mode. However, those forecasting models can also be used as standalone models. The growth and success of deep learning has largely been attributed to the availability of big data and high end computational power along with the theoretical advancement . In this thesis, multiple deep learning models are built for imputing the missing values and also for forecasting. The data used in building these deep learning models comprise California weather data, California strawberry yield, California strawberry farm-gate prices, USA corn yield data, Brent oil type daily prices and a synthetic time series dataset. For imputation, mean squared error is used as an metric to gauge the performance of imputation whereas for forecasting a new aggregated error measure (AGM) is proposed in this thesis which combines mean absolute error, mean squared error and R2 which is the coefficient of determination. Different models are found to be optimal for different time series. These models are illustrated in the recommendation framework developed in the thesis. Different stacking ensemble techniques such as voting regressor and stacking ML ensemble are then utilized to have better imputation results. The experiments show that the voting regressor yields the best imputation results. To gauge the robustness of the imputation framework, different time series are assessed. The imputed data is used for forecasting and the forecasting results are compared with market deep and non-deep learning models. The results show the best imputation models recommended based on work with the synthesized datasets are in fact the best for the tested incomplete real datasets with Mean Absolute Scaled Error (MASE) <1 i.e. better than the naive forecasting model. Also, it is found that the best imputation models have higher impact on reducing the forecasting errors compared to other deep or non-deep imputation models found in literature and market

Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas

AI Aided Tools for Fresh Produce Yield and Price Forecasting: Deep Learning Approaches

It is important to have an accurate estimate of the yields and prices of fresh produce (FP) for maintaining an effective 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 effectively 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. Choosing the most effective input parameters for forecasting strawberry yields and prices is investigated. 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. The effect of using soil input parameters on yield forecasting is further studied. To study the effect of static soil parameters on forecasting performance, the compound DL model SeriesNet with GRU is used to forecast the annual apple yield using the static and dynamic soil parameters. The county level annual apple yield forecast, using both static and dynamic parameters together, proves to give promising results, it reduces the forecasting AGM by around 34% compared to the case of excluding the static parameter and only using the dynamic parameters set. It is also found that, on using an augmented training set to train the DL model improves the AGM value by around 12% on testing with the non-augmented test set. To generalize the findings, transfer learning technique is utilized amongst the yield forecasting 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. First, the proposed DL model is trained using station-based soil data input mapped to the strawberry yield as output. The weights obtained from this learning are transferred to the raspberry yield forecasting ensemble model with minimal retraining. It is found that the transfer learning gives comparable results to training from scratch and reduces the processing time by half

Sensory Analysis and Consumer Research in New Product Development

Sensory analysis and consumer research are relevant tools in innovation andnew product development, from design to commercialization. This Special Issuehas collected 13 valuable scientific contributions, including 1 review, 12 originalresearch articles and an editorial. The SI provides an interesting outlookand better understanding of sensorial analysis with the different techniques andconsumer research on new product development. Important practical applicationshave been reported on the development of different novel, functional andenhanced products (meat, fish, biscuits, yogurt, porridge, hybrid meat, molecularproducts, etc.), which helps increase knowledge in this field. This SI isvery useful for both present and future uses for the different players involved inthis kind of product development (industry, companies, researchers, scientists,marketing, merchandising, consumers, etc.)

Southeast South Dakota Experiment Farm Annual Progress Report, 2022

This is an annual report of the research program at the Southeast South Dakota Research Farm in cooperation with South Dakota Agricultural Experiment Station and the SDSU College of Agriculture, Food, and Environmental Sciences and has special significance for those engaged in agriculture and the agriculturally related businesses in the ten county area of Southeast South Dakota. The results shown are not necessarily complete or conclusive. Interpretations given are tentative because additional data resulting from continuation of these experiments may result in conclusions different from those based on any one year