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 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 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 MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6578
Author(s):  
Ivan Vaccari ◽  
Giovanni Chiola ◽  
Maurizio Aiello ◽  
Maurizio Mongelli ◽  
Enrico Cambiaso
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Iot Devices ◽  
Definition Of

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

scholarly journals Minimizing Device-to-Device Variation in the Spectral Response of Portable Spectrometers

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Sunwoong Choi ◽  
Youngsik Kim ◽  
Jae-Hyung Lee ◽  
Hanjong You ◽  
Byung-Jun Jang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spectral Response ◽  
Critical Issue ◽  
Image Sensor ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Device To Device ◽  
Portable Spectrometers ◽  
Spectral Responses

As portable spectrometers have been developed, the research of spectral analysis has evolved from a traditional laboratory-based closed environment to a network-connected open environment. Consequently, its application areas are expanding in combination with machine learning techniques. The device-to-device variation in the spectral response of portable spectrometers is a critical issue in a machine learning-based service scenario since the classification performance is highly dependent on the consistency of spectral responses from each spectrometer. To minimize device-to-device variation, a cuboid prism is employed instead of a combination of mirrors and prism to construct an optical system for the spectrometer. The spectral responses are calibrated to correct pixel shift on the image sensor. Experimental results show that the proposed method can minimize the device-to-device variation in spectral response of portable spectrometers.

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.

MODELING PRESENT AND PROSPECTIVE DISTRIBUTION OF PHYTEUMA GENUS IN CARPATHIAN REGION WITH MACHINE LEARNING TECHNIQUES USING OPEN CLIMATIC AND SOIL DATA

2021 ◽  
Author(s):  
Alexander Mkrtchian
Keyword(s):  
Machine Learning ◽  
Open Data ◽  
Boosted Regression Trees ◽  
Machine Learning Techniques ◽  
Climate Projections ◽  
Climatic Data ◽  
Carpathian Region ◽  
Distribution Modeling ◽  
Learning Techniques ◽  
Plant Taxon

"Species distribution modeling can be effectively carried out using open data and data analysis tools with machine learning techniques. Modeling of the distribution of Phyteuma genus in the Carpathian region has been carried out with data from the GBIF database, climatic data from the Worldclim database, and soil properties data from Soilgrids soil information system. Spatial distribution modeling was accomplished with machine learning techniques that have marked advantages over more traditional statistical methods, like the ability to fit complex nonlinear relationships common in ecology. Four methods have been examined: Maxent, Random Forest, Artificial Neural Networks (ANN), and Boosted Regression Trees. AUC and TSS criteria calculated for testing data with cross-validation have been applied for assessing the performance of the models and to tune their parameters. ANN with a reduced set of predictor variables (6 from initial 21) appeared to fare the best and was applied for predictive modeling. Prospective data based on future climate projections from Worldclim were input to the model to get the prospective distribution of the plant taxon considering expected climate changes under different RCPs"

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