Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models

Maskey, Mahesh L.; Pathak, Tapan B.; Dara, Surendra K.

doi:10.3390/atmos10070378

Cited by 23 publications

(9 citation statements)

References 44 publications

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“…The result also showed that six VIs worked better than LAI as yield estimators. Maskey et al [113] utilized predictive principal component regression (PPCR), neural network (NN), and random forest (RF) models to forecast strawberry yield using 26 parameters related to leaf and canopy properties, soil characteristics, and weather conditions. Each of the selected weather parameters was highly correlated with strawberry yield, and the neural network (NN) analysis provided the best prediction accuracy (95%).…”

Section: Strawberry Yield Predictionmentioning

confidence: 99%

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021

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Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

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Section: Strawberry Yield Predictionmentioning

confidence: 99%

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021

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“…Various methods are used for yields prediction like Artificial Neural Networks [6,7], K-Nearest Neighbors [8,4] and Simple Long Short Term Memory Networks (LSTM) [9]. The weather parameters are used as input parameters to predict the yields in [10]; while in [11,5], the corresponding prices to strawberry yields are predicted using various DL compound models like ConvLSTM, CNN-LSTM, CNN-LSTM-GRU with attention along with DL ensemble models.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters

Chaudhary,

Gastli,

Nassar

et al. 2021

Preprint

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Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modeling. We propose here an alternate approach based on deep learning algorithms for forecasting strawberry yields and prices in Santa Barbara county, California. Building the proposed forecasting model comprises three stages: first, the station-based ensemble model (ATT-CNN-LSTM-SeriesNet_Ens) with its compound deep learning components, SeriesNet with Gated Recurrent Unit (GRU) and Convolutional Neural Network LSTM with Attention layer (Att-CNN-LSTM), are trained and tested using the station-based soil temperature and moisture data of Santa Barbara as input and the corresponding strawberry yields or prices as output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which is an ensemble model of Convolutional Neural Network LSTM (CNN-LSTM) models, is trained and tested using satellite images of the same county as input mapped to the same yields and prices as output. These two ensembles forecast strawberry yields and prices with minimal forecasting errors and highest model correlation for five weeks ahead forecasts. Finally, the forecasts of these two models are ensembled to have a final forecasted value for yields and prices by introducing a voting ensemble. Based on an aggregated performance measure (AGM), it is found that this voting ensemble not only enhances the forecasting performance by 5% compared to its best performing component model but also outperforms the Deep Learning (DL) ensemble model found in literature by 33% for forecasting yields and 21% for forecasting prices.

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“…Hence, if enough water is available, full irrigation is recommended. Seventh, Maskey and colleagues [8] published a paper exploring the correlation of various environmental factors affecting the yields of strawberries grown in an open field. In addition, they use the principal component regression method, two pattern recognition techniques such as a one-layer neural network model, and the random forest classification method to evaluate yield prediction.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method

Cho

Park

et al. 2020

Applied Sciences

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In this study, we propose a new agricultural data analysis method that can predict the weight during the growth stages of the field onion using a functional regression model. We have used onion weight on growth stages as the response variable and six environmental factors such as average temperature, average ground temperature, rainfall, wind speed, sunshine, and humidity as the explanatory variables in the functional regression model. We then define a least minimum integral squared residual (LMISE) measure to obtain an estimate of the function regression coefficient. In addition, a principal component regression analysis was applied to derive the estimates that minimize the defined measures. Next, to evaluate the performance of the proposed model, data were collected, and the following results were identified through analyses of the collected data. First, through graphical and correlation analysis, the ground temperature, mean temperature, and humidity have a very significant effect on the onion weights, but environmental factors such as wind speed, sunshine, and rainfall have a small negative effect on onion weights. Second, through functional regression analysis, we can determine that the ground temperature, sunshine, and precipitation have a significant effect on onion growth and are essential in the goodness-of-fit test. On the other hand, wind speed, mean temperature, and humidity did not significantly affect onion growth. In conclusion, to promote onion growth, the appropriate ground temperature and amount of sunshine are essential, the rainfall and the humidity must be low, and the appropriate wind or mean temperature must be maintained.

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Weather Based Strawberry Yield Forecasts at Field Scale Using Statistical and Machine Learning Models

Cited by 23 publications

References 44 publications

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters

Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method

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