Autumn Crop Yield Prediction using Data-Driven Approaches:- Support Vector Machines, Random Forest, and Deep Neural Network Methods

Dang, Chaoya; Liu, Ying; Yue, Hui; Qian, Jiaxin; Zhu, Rong

doi:10.1080/07038992.2020.1833186

Cited by 61 publications

(21 citation statements)

References 74 publications

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“…RF algorithms have some limitations which the present research encountered and researchers should be aware of. Dang et al [ 79 ] highlighted that the lower performance of RFR autumn crop yield prediction compared to Support Vector Regression (SVR) and Deep Neural Network (DNN). This was attributed to its inability to make predictions beyond the range of values of the training set data, the tendency of overfitting when modeling noisy data, and discreteness of output values defined by categories (however narrowly defined), which would otherwise give continuous range of output values provided by, e.g., SVR.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia

Pang

Chang

Yang

2022

Sensors

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Wheat accounts for more than 50% of Australia’s total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet’s high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well (R2 = 0.86, RMSE = 0.18 t ha−1). RF models for individual paddocks in VIC (R2 = 0.89, RMSE = 0.15 t ha−1) and NSW (R2 = 0.87, RMSE = 0.07 t ha−1) performed well, but moderate performance was seen for SA (R2 = 0.45, RMSE = 0.25 t ha−1). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded ‘big data’ for regional as well as local-scale yield prediction.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia

Pang

Chang

Yang

2022

Sensors

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“…These studies show us that crop yield and development are still dependent on many variables and have a complex structure (Schauberger et al, 2020). These prediction efforts have been conducted on a regional and large scale (Dang et al, 2021;Gómez et al, 2021) as well as on a field scale (Engen et al, 2021;Cao et al, 2021a) in diverse climates. To predict crop yield, crop growth simulation models (AquaCrop, DSSAT, WOFOST, EPIC, VIC, etc.)…”

Section: Introductionmentioning

confidence: 99%

Crop Yield Prediction based on Reanalysis and Crop Phenology Data in the Agroclimatic Zones

Yeșilköy¹,

Demir²

2023

Preprint

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Crop yield and phenological stages are remarkably sensitive to not only environmental factors like atmospheric conditions and physical properties of soils but also agricultural activities. Accurate crop yield prediction plays a crucial role in food security and agricultural sustainability. There are several approaches that a wide range of researchers have tried to predict crop yield at different scales. In this study, we tested AgERA5 reanalysis product and crop phenological stage data to predict winter wheat yields in the agricultural lands of the agroclimatic regions of Turkey. The main objective is to propose a deep learning approach based on the combination of the reanalysis, which was extracted for the agricultural lands of the five most productive agroclimatic zones, and crop phenology data to predict winter wheat yields. Five performance indicators, such as normalized root mean squared error (NRMSE), mean absolute percentage error (MAPE), root mean squared error (RMSE), Nash-Sutcliffe Efficiency (NSE), and coefficient of determination (R2), are chosen to test the model’s accuracy and effectiveness. We have obtained promising findings and suggested that AgERA5 reanalysis data can be used as an input for the crop yield prediction of winter wheat with an error below 10% and a coefficient of determination above 0.9.

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“…These approaches have the ability to predict linear and nonlinear agricultural architecture. From the learning process, these methods were obtained in ML agricultural framework [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Classification and yield prediction in smart agriculture system using IoT

Gupta

Nahar

2022

J Ambient Intell Human Comput

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The Modern agriculture industry is datacentred, precise and smarter than ever. Advanced development of Internet-of-Things (IoT) based systems redesigned "smart agriculture". This emergence in innovative farming systems is gradually enhancing the crop yield, reduces irrigation wastages and making it more profitable. Machine learning (ML) methods achieve the requirement of scaling the learning performance of the model. This paper introduces a hybrid ML model with IoT for yield prediction. This work involves three phases : preprocessing, feature selection(FS) and classification. Initially, the dataset is preprocessed and FS is done on the basis of Correlation based FS (CBFS) and the Variance Inflation Factor algorithm (VIF). Finally, a two-tier ML model is proposed for IoT based smart agriculture system. In the first tier, the Adaptive k-Nearest Centroid Neighbour Classifier (aKNCN) model is proposed to estimate the soil quality and classify the soil samples into different classes based on the input soil properties. In the second tier, the crop yield is predicted using the Extreme Learning Machine algorithm (ELM). In the optimized strategy, the weights are updated using modified Butterfly Optimization algorithm (mBOA) to improve the performance accuracy of ELM with minimum error values. PYTHON is the implementation tool for evaluating the proposed system. Soil dataset is utilized for performance evaluation of the proposed prediction model. Various metrics are considered for the performance evaluation such as accuracy, RMSE, R2, MSE, MedAE, MAE, MSLE, MAPE and Explained Variance Score (EVS).

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Autumn Crop Yield Prediction using Data-Driven Approaches:- Support Vector Machines, Random Forest, and Deep Neural Network Methods

Cited by 61 publications

References 74 publications

Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia

Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia

Crop Yield Prediction based on Reanalysis and Crop Phenology Data in the Agroclimatic Zones

Classification and yield prediction in smart agriculture system using IoT

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