scholarly journals AGRICULTURAL DATA ANALYSIS

2021 ◽  
Vol 9 (08) ◽  
pp. 807-815
Author(s):  
Shobana S. ◽  
◽  
M. Sujithra ◽  
Keyword(s):  
Experimental Data ◽  
Crop Production ◽  
Commodity Prices ◽  
Machine Learning Techniques ◽  
Climatic Data ◽  
Optimal Parameters ◽  
Agriculture Sector ◽  
Factors Influencing ◽  
Agricultural Planning ◽  
Learning Techniques

In agriculture sector where farmers and agribusinesses have to make innumerable decisions every day and intricate complexities involves the various factors influencing them. An essential issue for agricultural planning intention is the accurate yield estimation for the numerous crops involved in the planning. Data mining techniques are necessary approach for accomplishing practical and effective solutions for this problem. Agriculture has been an obvious target for big data. Environmental conditions, variability in soil, input levels, combinations and commodity prices have made it all the more relevant for farmers to use information and get help to make critical farming decisions. This paper focuses on the analysis of the agriculture data and finding optimal parameters to maximize the crop production using Machine learning techniques like random forest regressor and Linear Regression. Mining the large amount of existing crop, soil and climatic data, and analysing new, non-experimental data optimizes the production and makes agriculture more resilient to climatic change.

scholarly journals Crop Production Analysis using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 572-575
Author(s):  
K Krishna Chaitanya
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Critical Issue ◽  
Machine Learning Techniques ◽  
Climatic Data ◽  
Agricultural Industry ◽  
Agricultural Planning ◽  
Learning Techniques ◽  
Yield Calculation ◽  
Best Parameters

As we all know, in the agricultural industry, farmers and agribusinesses must make countless decisions every day, and the different elements influencing them are complex. The proper yield calculation for the different crops involved in the planning is a critical issue for agricultural planning. Data mining techniques are a critical component of achieving practical and successful solutions to this issue. Agriculture has always been a natural fit for big data. Environmental conditions, soil variability, input amounts, combinations, and commodity pricing have all made it more important for farmers to use data and seek assistance when making vital farming decisions. This research focuses on analyzing agricultural data and determining the best parameters to maximize crop output using machine learning techniques such as Random Forest, Decision Tree and Linear Regression, which can achieve high accuracy. Mining current crop, soil, and climatic data, as well as evaluating new, non-experimental data, improves production and makes agriculture more robust to climate change.

scholarly journals Review of Crop Yield Prediction using Machine Learning Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 4626-4628
Author(s):  
Kale Jaydeep Narayan
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Soil Types ◽  
Yield Prediction ◽  
Growth Season ◽  
Learning Techniques ◽  
Agricultural Yield ◽  
Decision Making Tool

Machine learning (ML) could be a helpful decision-making tool for predicting crop yields, in addition as for deciding what crops to plant and what to try throughout the crop's growth season. To help agricultural yield prediction studies, variety of machine learning techniques are used. I performed a literature review (LR) to extract and synthesize the algorithms and options employed in crop production prediction analysis. Temperature, rainfall, and soil types are most common measure used in the prediction as per my knowledge, whereas Artificial Neural Networks is the foremost normally used methodology in these models.

scholarly journals Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lasini Wickramasinghe ◽  
Rukmal Weliwatta ◽  
Piyal Ekanayake ◽  
Jeevani Jayasinghe
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Rice Yield ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Climatic Data ◽  
Climate Variables ◽  
Squared Error ◽  
Learning Techniques ◽  
The Relationship

This paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). The performance of each model was assessed in terms of the mean squared error (MSE), correlation coefficient (R), mean absolute percentage error (MAPE), root mean squared error ratio (RSR), BIAS value, and the Nash number, and it was found that the GPR-based model is the most accurate among them. Climate data collected until early 2019 (Maha season of year 2018) were used to develop the model, and an independent validation was performed by applying data of the Yala season of year 2019. The developed model can be used to forecast the future rice yield with very high accuracy.

scholarly journals Smart Agriculture Monitoring System using ML

2020 ◽  
Vol 9 (4) ◽  
pp. 2404-2407
Keyword(s):  
Machine Learning ◽  
Temperature Sensor ◽  
Agricultural Sector ◽  
Cost Effective ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Effective Manner ◽  
Cloud Computation

Agriculture plays vital role in every individual’s life. As the technology improves, agricultural sector has been improving by the needs of people. Basically, the idea here deals with monitoring of weather, temperature, soil moisture and other agriculture related aspects. The objective of this paper is to upgrade -growth probability. So by making use of Advance technologies good and efficient crop can be yield. Cloud (Firebase) is typically used to store the pre-computed data (data sets) and the data from the efficiency of agriculture sector. This idea comprises of Machine Learning techniques, Cloud Computation [5] and IoT. Here we will use machine learning techniques for predicting crop sensors and comparison between these. IoT includes NPK sensors, temperature sensor, and humidity sensor. The mechanism goes like this- initially the data from humidity, temperature sensor will be noted and NPK sensors will be placed in the soil, the values from the sensors will be sent to cloud by making use of any communication technology (ZigBee, IoT gateway devices). In cloud comparison of pre-computed data and data from sensors happens by making use of machine learning. The outcome from cloud may be stored in the server (Admin) or directly be notified to authorized person of the land in the form for notification. By taking all these parameters into consideration, we can predict the best suitable crop that can be grown and farmers will earn profit in a cost-effective manner.

scholarly journals Pattern Discovery in White Etching Crack Experimental Data Using Machine Learning Techniques

2019 ◽  
Vol 9 (24) ◽  
pp. 5502 ◽  
Author(s):  
Baher Azzam ◽  
Freia Harzendorf ◽  
Ralf Schelenz ◽  
Walter Holweger ◽  
Georg Jacobs
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Feature Selection Method ◽  
Chemical Compounds ◽  
Machine Learning Techniques ◽  
Oil Composition ◽  
The Past ◽  
Learning Techniques ◽  
Physical Tests ◽  
The Relationship

White etching crack (WEC) failure is a failure mode that affects bearings in many applications, including wind turbine gearboxes, where it results in high, unplanned maintenance costs. WEC failure is unpredictable as of now, and its root causes are not yet fully understood. While WECs were produced under controlled conditions in several investigations in the past, converging the findings from the different combinations of factors that led to WECs in different experiments remains a challenge. This challenge is tackled in this paper using machine learning (ML) models that are capable of capturing patterns in high-dimensional data belonging to several experiments in order to identify influential variables to the risk of WECs. Three different ML models were designed and applied to a dataset containing roughly 700 high- and low-risk oil compositions to identify the constituting chemical compounds that make a given oil composition high-risk with respect to WECs. This includes the first application of a purpose-built neural network-based feature selection method. Out of 21 compounds, eight were identified as influential by models based on random forest and artificial neural networks. Association rules were also mined from the data to investigate the relationship between compound combinations and WEC risk, leading to results supporting those of previous analyses. In addition, the identified compound with the highest influence was proved in a separate investigation involving physical tests to be of high WEC risk. The presented methods can be applied to other experimental data where a high number of measured variables potentially influence a certain outcome and where there is a need to identify variables with the highest influence.

scholarly journals A Review on Agriculture Crop Prediction Techniques Using Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 484-488
Author(s):  
Firdous Hina
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Comprehensive Overview ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Agricultural Yield ◽  
Post Harvesting ◽  
Prediction Techniques

Abstract: Machine learning is a useful decision-making tool for predicting crop yields, as well as for deciding what crops to plant and what to do during the crop's growth season. To aid agricultural yield prediction studies, a number of machine learning techniques have been used. We employed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features used in crop production prediction research in this investigation This paper provides a comprehensive overview of the most recent machine learning applications in agriculture, with a focus on pre-harvesting, harvesting, and post-harvesting issues The papers have been studied in depth, analysed the methodology and features employed, and made recommendations for future study. Temperature, rainfall, and soil type are the most commonly utilised features, according to our data, while Artificial Neural Networks are the most commonly employed method in these models.

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