PredictingRice crop yield using Bayesian networks

Gandhi, Niketa; Armstrong, Leisa; Petkar, Owaiz

doi:10.1109/icacci.2016.7732143

Cited by 44 publications

(19 citation statements)

References 11 publications

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“…Some researchers look into the integration of process-based and statistical modeling in an effort to achieve higher accuracy (Michael et al 2017). Under the rapid development of computing capabilities in recent years, artificial intelligence approaches, such as Random Forest (RF) (Saeed et al 2017), artificial neural network (Alvarez 2009), Bayesian network (Gandhi et al 2016), semiparametric neural network (Crane-Droesch 2018), and convolutional neural network (You et al 2017, Yang et al 2019, have gradually been applied to extract patterns for agricultural yield estimation. These studies, however, often simplify the temporal cumulative effect of crop growth and the spatial heterogeneity problem.…”

Section: Introductionmentioning

confidence: 99%

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Lin

Zhong

Wang

et al. 2020

Environ. Res. Lett.

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Large-scale crop yield estimation is critical for understanding the dynamics of global food security. Understanding and quantifying the temporal cumulative effect of crop growth and spatial variances across different regions remains challenging for large-scale crop yield estimation. In this study, a deep spatial-temporal learning framework, named DeepCropNet (DCN), has been developed to hierarchically capture the features for county-level corn yield estimation. The temporal features are learned by an attention-based long short-term memory network and the spatial features are learned by the multi-task learning (MTL) output layers. The DCN model has been applied to quantify the relationship between meteorological factors and the county-level corn yield in the US Corn Belt from 1981 to 2016. Three meteorological factors, including growing degree days, killing degree days, and precipitation, are used as time-series inputs. The results show that DCN provides an improved estimation accuracy (RMSE=0.82 Mg ha −1 ) as compared to that of conventional methods such as LASSO (RMSE=1.14 Mg ha −1 ) and Random Forest (RMSE=1.05 Mg ha −1 ). Temporally, the attention values computed from the temporal learning module indicate that DCN captures the temporal cumulative effect and this temporal pattern is consistent across all states. Spatially, the spatial learning module improves the estimation accuracy based on the regional specific features captured by the MTL mechanism. The study highlights that the DCN model provides a promising spatial-temporal learning framework for corn yield estimation under changing meteorological conditions across large spatial regions.

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Section: Introductionmentioning

confidence: 99%

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Lin

Zhong

Wang

et al. 2020

Environ. Res. Lett.

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“…Rice is one of the most important food crops of India. It is cultivated all over the country and contributes more than 40% of total food grain production [18]. Given the importance of rice to world's food security, any improvements in the forecasting of rice crop yield under different climatic and cropping scenarios will be beneficial [5].…”

Section: Discussionmentioning

confidence: 99%

Rice Crop Yield Forecasting Using Random Forest Algorithm SML

Narasimhamurthy¹

2017

IJRASET

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Rice is the principal and dominant crop of India after wheat. India being at second position in the world after China often cited as main contributor to the rice production and accounts for 20% of the world's total production. The amount of hectares in India under rice cultivation is as high as 40 million hectares in 20 states. India is also the largest exporter of rice in the world crossing 100 million tones. The sustainability and productivity of rice growing areas is dependent on suitable climatic conditions. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in better decision making in terms of agronomy and crop choice. To predict the crop yield in future accurately, Random Forest, a most powerful and popular supervised machine learning algorithm is used.

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“…Nitrogen, The result of MLR model is cross verified using Density based clustering. In [3] authors explored the use of Bayesian Networks for crop yield prediction. By using parameters like precipitation, temperature range, evaporation, transpiration, area, production and yield for the Kharif season a Bayesian model is built to predict crop yield.…”

Section: Related Workmentioning

confidence: 99%

Yield Forecasting for Indian Crops with Ensemble Model

Lokesh¹,

Senthil²

2019

IJRTE

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In fast developing economies like India, there is fast depletion of agricultural lands and many people migrate to cities leaving agriculture. In these conditions effective yield prediction of crops is a must for planning food policies and securing food for the people. Import only at time of huge price rise is not a solution and predictive import of crops based on yield predicted is a way to keep food inflation under control. Towards this end, an Ensemble model for prediction of yield of India Rabbi Crops is proposed in this work. An Ensemble machine learning model is built based on past histories and macro climatic and monsoon conditions to forecast the yield for crops in work.

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PredictingRice crop yield using Bayesian networks

Cited by 44 publications

References 11 publications

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Rice Crop Yield Forecasting Using Random Forest Algorithm SML

Yield Forecasting for Indian Crops with Ensemble Model

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