A prototype model for continuous agriculture field  monitoring and assessment

Bhimanpallewar, Ratnmala; Narasingarao, M. R.

doi:10.14419/ijet.v7i2.7.10288

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Either we can have sensor-based device which will sense actual field features and record the dataset. According to features we want to monitor; device can be integrated with respective sensors (available in market) and microcontroller to control the data recording [14] and storing process. This method can be referred as monitoring feature values.…”

Section: Collecting Data Using Feature Valuesmentioning

confidence: 99%

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Precision in Agriculture Decision Making Based on Machine Learning

Bhimanpallewar¹,

Rao²

2021

Technology in Agriculture

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Farming is the one of the major occupations in India. Increase in population is increasing the demand of food, whereas soil degradation causing decrease in yield. Technology is contributing in agriculture domain through software and hardware enhancement. One of the software-based contribution is for predicting the suitable crop. Same field can be suitable for one crop and not for another one, so it is better to choose the one which can lead to better yield. There are many predictive algorithms available. Algorithms which can work for suitability analysis need to test and choose the best one. Such predictive algorithms need dataset in appropriate format. Once the quality data is available correct predictions can be made. Data mining, machine learning are the branches comprise of algorithms, which can be trained based on dataset. Here we are introducing algorithms for decision making based on field data.

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Section: Collecting Data Using Feature Valuesmentioning

confidence: 99%

“…A monitoring system with microcontroller and accessories [14] to sense the nearby features like. All the accessories like above can be used to monitor the actual field.…”

Section: Monitoring Feature Valuesmentioning

confidence: 99%

Precision in Agriculture Decision Making Based on Machine Learning

Bhimanpallewar¹,

Rao²

2021

Technology in Agriculture

Self Cite

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“…Cloud is used to store input and output data before they are retrieved by owner's smartphone. ESP8266 Wi-Fi Module is a self-contained SOC (System On a Chip) with integrated TCP/IP protocol stack that can give any microcontroller access to Wi-Fi network [11].…”

Section: Figure 2: Chicken Coop Designmentioning

confidence: 99%

Urbanized Chicken Coop Monitoring System using IoT

Karim¹

2019

IJATCSE

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This paper presents the development of Urbanized Chicken Coop Monitoring system using IoT for residential area. Mostly, people who live in a residential area are working people. They usually have the problem to monitor the chicken coop regularly. With the implementation of the IoT in this urbanized chicken coop, monitoring chicken will not become a big trouble. The input of this system is from sensors that will be controlled by microcontroller and will be used to send data to the cloud. Then, to complete the system, the data stored in cloud are then sent to the chicken coop's owner's smart phone for notification. The outcome of this project is to provide healthy chicken for the people in any residential and simplifying the coop monitoring.

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“…This would show a coherent plot from 100 to 500, which would be difficult to open. Subsequently, to solve this issue, I initially used [10,20,30,40,50] with respect to x. Until then, I renamed those using xticks.…”

Section: Random Forest Classifiermentioning

confidence: 99%

Heart Disease Prediction using Machine Learning Techniques

Razia¹,

Babu²,

Baradwaj³

et al. 2019

IJRTE

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Nowadays, heart disease has become a major disease among the people irrespective of the age. We are seeing this even in children dying due to the heart disease. If we can predict this even before they die, there may be huge chances of surviving. Everybody has various qualities of beat rate (pulse rate) and circulatory strain (blood pressure). We are living in a period of data. Due to the rise in the technology, the amount of data that is generated is increasing daily. Some terabytes of data are being produced and stored. For example, the huge amount of data about the patients is produced in the hospitals such as chest pain, heart rate, blood pressure, pulse rate etc. If we can get this data and apply some machine learning techniques, we can reduce the probability of people dying. In this paper we have done survey using different classification and grouping strategies, for example, KNN, Decision tree classifier, Gaussian Naïve Bayes, Support vector machine, Linear regression, Logistic regression, Random forest classifier, Random forest regression, linear descriptive analysis. We have taken the 14 attributes that are present in the dataset as an input and applying on the dataset which is taken from the UCI repository to develop and accurate model of predicting the heart disease contains colossal (huge) therapeutic (medical) information. In the proposed research, the exhibition of the conclusion model is acquired by using utilizing classification strategies. In this paper proposed an accuracy model to predict whether a person has coronary disease or not. This is implemented by comparing the accuracies of different machine-learning strategies such as KNN, Decision tree classifier, Gaussian Naïve Bayes, SVM, Logistic regression, Random forest classifier, Linear regression, Random forest regression, linear descriptive analysis

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A prototype model for continuous agriculture field monitoring and assessment

Cited by 4 publications

References 8 publications

Precision in Agriculture Decision Making Based on Machine Learning

Precision in Agriculture Decision Making Based on Machine Learning

Urbanized Chicken Coop Monitoring System using IoT

Heart Disease Prediction using Machine Learning Techniques

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