Rice Crop Yield Forecasting Using Random Forest Algorithm SML

Narasimhamurthy, Venkata

doi:10.22214/ijraset.2017.10176

Cited by 15 publications

(14 citation statements)

References 10 publications

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“…Different approaches to estimate crop yield have been developed, and the application of ML algorithms on databases composed of vegetation indices (VIs) derived from satellite imagery data is being largely adopted [18][19][20][21]. The random forest (RF) algorithm currently stands out among the others in predicting crop yield on datasets composed of VIs (sunflower [22], sugarcane [23], and rice [24]) or spectral bands (corn [25]). Using ML to predict yield from VIs has shown potential in discovering new interactions [9].…”

Section: Introductionmentioning

confidence: 99%

Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

Wei

Maldaner

Ottoni

et al. 2020

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Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield.

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Section: Introductionmentioning

confidence: 99%

Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

Wei

Maldaner

Ottoni

et al. 2020

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“…The use of random forests has been popular in predicting a continuous response variable like a regression that fits an ensemble of decision tree models to a set of data [63]. The use of random forest (RF) has been widely recognized in the literature for crop prediction applications [34,[63][64][65].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

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

2019

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Strawberry is a high value and labor-intensive specialty crop in California. The three major fruit production areas on the Central Coast complement each other in producing fruits almost throughout the year. Forecasting strawberry yield with some lead time can help growers plan for required and often limited human resources and aid in making strategic business decisions. The objectives of this paper were to investigate the correlation among various weather parameters related with strawberry yield at the field level and to evaluate yield forecasts using the predictive principal component regression (PPCR) and two machine-learning techniques: (a) a single layer neural network (NN) and (b) generic random forest (RF). The meteorological parameters were a combination of the sensor data measured in the strawberry field, meteorological data obtained from the nearest weather station, and calculated agroclimatic indices such as chill hours. The correlation analysis showed that all of the parameters were significantly correlated with strawberry yield and provided the potential to develop weekly yield forecast models. In general, the machine learning technique showed better skills in predicting strawberry yields when compared to the principal component regression. More specifically, the NN provided the most skills in forecasting strawberry yield. While observations of one growing season are capable of forecasting crop yield with reasonable skills, more efforts are needed to validate this approach in various fields in the region.

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“…The tree is then built by methods for recursive apportioning until the present leaf hubs contain just examples of a solitary class or until no test offers any improvement. Be that as it may, since most genuine informational collections contains noise, and since by and large the traits have restricted prescient power, this tree developing system frequently results in a complex tree with numerous inward hubs that overfits the information SML Venkata Narasimhamurthy and AVS Pavan Kumar [9], have been very specific in their work of analyzing the crop yield, especially for rice grown in India. Rice is the key and prevailing crop of India after wheat.…”

Section: Decision Trees-mentioning

confidence: 99%

Data Analytics on Agrometeorological Parameters for Building A Utility System for Farmer Community

Sowmya¹,

Mundada²,

Hegde³

et al. 2019

IJRTE

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The day-to-day evolution of the world is throwing many challenges, thereby demanding the humans to be on par with the modern tools and technologies that are on offer. These technologies are contributing to the overall growth across all the domains. One such domain that is highly affected by the modern tools and techniques is Agriculture. Gone are the days, when only a handful methodologies and tools were utilized to understand about agriculture. Although the farmers are sceptical to the modernization, the end result has been encouraging at many a times. With the use of analytics, one can get to know more about the type of soil, crops and fertilizers, amount of water to be utilized depending on the climatic conditions and thereby have an effective yield. In this work, the elementary task of portraying the effect of climatic conditions on the production of different varieties of crops is carried out. Also, the application of Multivariate Linear Regression and Artificial Neural Network (ANN) techniques provides a significance outcome in the prediction of yield with the focus on Ragi and Rice. The work provides an accuracy of 65% with the 3-fold cross validation technique and 68% accuracy with ANN model.

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Rice Crop Yield Forecasting Using Random Forest Algorithm SML

Cited by 15 publications

References 10 publications

Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

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

Data Analytics on Agrometeorological Parameters for Building A Utility System for Farmer Community

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