This document describes a study that uses machine learning algorithms to recommend crops, fertilizers, and pesticides to farmers based on soil properties and environmental conditions. The study collects data on factors like soil pH, moisture, temperature, and rainfall from soil testing laboratories and online sources. It then uses random forest, KNN, and decision tree algorithms to analyze the data and make recommendations. The random forest algorithm achieved the highest accuracy of 97% compared to 78% for decision tree and 83% for KNN. The goal is to help farmers select optimal crops and maximize yields by accounting for land conditions. The researchers conclude machine learning is an effective approach that can improve agricultural productivity and economic outcomes for farmers.
Pesticide recommendation system for cotton crop diseases due to the climatic ...IJMREMJournal
Data mining is a process of extracting knowledge from a vast database using tools and techniques. Data
mining plays an important role in decision making on issues related to many real-time problems such as
business, education, agriculture etc. Data mining in agriculture helps the farmers to decide on crop yield ratio,
water resource management, pesticides management and fertilizer management. Nowadays, climatic change is
one of the challenging problems in agriculture which has a greater impact on productivity. Many
researchers have contributed in the field of agriculture data mining i) To predict crop productivity, ii) water
management, iii) air pollution using the naïve bias and decision tree algorithms. The Proposed work is to
predict the diseases due to Climatic changes and recommended pesticide for the disease. Decision tree
algorithm is used to develop a recommendation system which helps to the farmer in the usage of pesticide for
the incidence of crop diseases.
Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many
sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in
reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and
conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior
weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly
and sustainably. In agriculture, it is the use of current information and
communication technologies.
An Efficient and Novel Crop Yield Prediction Method using Machine Learning Al...IIJSRJournal
The process of examining, filtering, and presenting data to obtain valuable information and make decisions is known as information analysis. Food resources are in high demand in countries like India, where they serve the population and help to secure the nation's security. Crop production is largely influenced by weather variations, soil quality, water availability, and fertilizer application, among other factors. The various types of soil play a significant effect in agricultural production. Recommending fertilizers to agriculturists may assist them in making better crop selection and maintenance decisions. Crop yield prediction can be done using a variety of studies using information and communication technology (ICT). Different sorts of mining techniques for data analysis and data acquisition can be widely used for a variety of purposes. Smart agriculture is a method of transmitting data from average farmers to skilled farmers.
Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, and public sector management. IJMRR is an international forum for research that advances the theory and practice of management. The journal publishes original works with practical significance and academic value.
Pesticide recommendation system for cotton crop diseases due to the climatic ...IJMREMJournal
Data mining is a process of extracting knowledge from a vast database using tools and techniques. Data
mining plays an important role in decision making on issues related to many real-time problems such as
business, education, agriculture etc. Data mining in agriculture helps the farmers to decide on crop yield ratio,
water resource management, pesticides management and fertilizer management. Nowadays, climatic change is
one of the challenging problems in agriculture which has a greater impact on productivity. Many
researchers have contributed in the field of agriculture data mining i) To predict crop productivity, ii) water
management, iii) air pollution using the naïve bias and decision tree algorithms. The Proposed work is to
predict the diseases due to Climatic changes and recommended pesticide for the disease. Decision tree
algorithm is used to develop a recommendation system which helps to the farmer in the usage of pesticide for
the incidence of crop diseases.
Internet of things (IoT) smart technology enables new digital agriculture. Technology has become necessary to address today's challenges, and many
sectors are automating their processes with the newest technologies. By maximizing fertiliser use to boost plant efficiency, smart agriculture, which is based on IoT technology, intends to assist producers and farmers in
reducing waste while improving output. With IoT-based smart farming, farmers may better manage their animals, develop crops, save costs, and
conserve resources. Climate monitoring, drought detection, agriculture and production, pollution distribution, and many more applications rely on the weather forecast. The accuracy of the forecast is determined by prior
weather conditions across broad areas and over long periods. Machine learning algorithms can help us to build a model with proper accuracy. As a result, increasing the output on the limited acreage is important. IoT smart farming is a high-tech method that allows people to cultivate crops cleanly
and sustainably. In agriculture, it is the use of current information and
communication technologies.
An Efficient and Novel Crop Yield Prediction Method using Machine Learning Al...IIJSRJournal
The process of examining, filtering, and presenting data to obtain valuable information and make decisions is known as information analysis. Food resources are in high demand in countries like India, where they serve the population and help to secure the nation's security. Crop production is largely influenced by weather variations, soil quality, water availability, and fertilizer application, among other factors. The various types of soil play a significant effect in agricultural production. Recommending fertilizers to agriculturists may assist them in making better crop selection and maintenance decisions. Crop yield prediction can be done using a variety of studies using information and communication technology (ICT). Different sorts of mining techniques for data analysis and data acquisition can be widely used for a variety of purposes. Smart agriculture is a method of transmitting data from average farmers to skilled farmers.
Authors are invited to submit theoretical or empirical papers in all aspects of management, including strategy, human resources, marketing, operations, technology, information systems, finance and accounting, business economics, and public sector management. IJMRR is an international forum for research that advances the theory and practice of management. The journal publishes original works with practical significance and academic value.
Selection of crop varieties and yield prediction based on phenotype applying ...IJECEIAES
In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.
Survey of Diesease Prediction on Plants with the Helps of IOTrahulmonikasharma
overall climate change is a diversity in the long-term weather patterns that indicates the regions of the world. The term "weather" refers to the short-term (daily) changes in temperature, wind, and precipitation of a region.With the up-gradation in data mining and its applications, data mining is extensively used to make smarter decisions in farming.Agricultural forecasting is the science that employ knowledge in weather data relating to atmospheric environment observed by instruments on the ground and by remote sensing. Most of the data need to be processed for generating various decisions such as cropping and scheduling of irrigation.Various meteorological data like- temperature, humidity, leaf wetness duration (LWD) plays the vital roles in the growth of microorganism responsible for disease.Effective forecasting of such diseases on the basis of climate data can help the farmers to take timely actions to restrain the diseases. This can also justify the use of pesticides, which are one of the source behind land pollution. This paper illustrate the study which is useful for farmers in order to make decision if there is change occur in environment. In this study we are going to implement application which give the notification to farmers, if there is change in environment so based on that changes which disease should be affected to plant such type of notification will be generated on farmers mobile.Weather based forecasting system can be treated as a part of the Agricultural Decision Support System (ADSS) which is Knowledge Based System (KBS). IoT device that collects data regarding physical parameters, using a sophisticated microcontroller platform, from various types of sensors, through different modes of communication and then uploads the data to the Internet.
Crop Prediction System using Machine Learningijtsrd
Indias economy is mostly based on agricultural yield growth and linked agro industry products, as it is an agricultural country. Rainwater, which is often unpredictable in India, has a significant impact on agriculture. Agriculture growth is also influenced by a variety of soil parameters, such as nitrogen, phosphorus, and potassium, as well as crop rotation, soil moisture, and surface temperature, as well as climatic factors such as temperature and rainfall. India is quickly advancing in terms of technical advancement. As a result, technology will benefit agriculture by increasing crop productivity, resulting in higher yields for farmers. The suggested project provides a solution for storing temperature, rainfall, and soil characteristics in order to determine which crops are suited for cultivation in a given area. This paper describes a system, implemented as an android application, that employs data analytics techniques to predict the most profitable crop based on current weather and soil conditions. The suggested system will combine data from a repository and the weather department using a machine learning algorithm Using Multiple Linear Regression, it is possible to anticipate the most suited crops based on current environmental circumstances. This gives a farmer a wide range of crops to choose from. As a result, the project creates a system that integrates data from diverse sources, performs data analytics, and conducts predictive analysis in order to improve crop production productivity and boost farmer profit margins over time. Machine learning, crop prediction, and yield estimation are some of the terms used in this paper. Manju D C | Murugan R "Crop Prediction System using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49725.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-processing/49725/crop-prediction-system-using-machine-learning/manju-d-c
Adoption of crop scheduling techniques in India for sugarcane and pomegranate
production has been disappointing. The challenge is to use state of the art technology to provide
practical and useful advice to farmers and further to convince farmers of the benefits of crop
scheduling by on-farm demonstration. The purpose of this project is to describe: 1. To expose the
system to the practical aspects of farming in order to refine it if necessary. 2. To evaluate the
accuracy of the system to predict crop growth and health. 3. A high technology system to provide
practical, real time cropping advice on climate situations. The system consists of a web-based
simulation model that estimates the recent, current and future crop status and yield from field
information and real time weather data. The system automatically generates and distributes simple
advice by SMS to farmers’ cellular phones. The system is evaluated on a small-scale sugarcane and
pomegranate scheme at Pandharpur, Maharashtra. Yields are not affected significantly and
profitability is enhanced considerably.
Selection of crop varieties and yield prediction based on phenotype applying ...IJECEIAES
In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.
Survey of Diesease Prediction on Plants with the Helps of IOTrahulmonikasharma
overall climate change is a diversity in the long-term weather patterns that indicates the regions of the world. The term "weather" refers to the short-term (daily) changes in temperature, wind, and precipitation of a region.With the up-gradation in data mining and its applications, data mining is extensively used to make smarter decisions in farming.Agricultural forecasting is the science that employ knowledge in weather data relating to atmospheric environment observed by instruments on the ground and by remote sensing. Most of the data need to be processed for generating various decisions such as cropping and scheduling of irrigation.Various meteorological data like- temperature, humidity, leaf wetness duration (LWD) plays the vital roles in the growth of microorganism responsible for disease.Effective forecasting of such diseases on the basis of climate data can help the farmers to take timely actions to restrain the diseases. This can also justify the use of pesticides, which are one of the source behind land pollution. This paper illustrate the study which is useful for farmers in order to make decision if there is change occur in environment. In this study we are going to implement application which give the notification to farmers, if there is change in environment so based on that changes which disease should be affected to plant such type of notification will be generated on farmers mobile.Weather based forecasting system can be treated as a part of the Agricultural Decision Support System (ADSS) which is Knowledge Based System (KBS). IoT device that collects data regarding physical parameters, using a sophisticated microcontroller platform, from various types of sensors, through different modes of communication and then uploads the data to the Internet.
Crop Prediction System using Machine Learningijtsrd
Indias economy is mostly based on agricultural yield growth and linked agro industry products, as it is an agricultural country. Rainwater, which is often unpredictable in India, has a significant impact on agriculture. Agriculture growth is also influenced by a variety of soil parameters, such as nitrogen, phosphorus, and potassium, as well as crop rotation, soil moisture, and surface temperature, as well as climatic factors such as temperature and rainfall. India is quickly advancing in terms of technical advancement. As a result, technology will benefit agriculture by increasing crop productivity, resulting in higher yields for farmers. The suggested project provides a solution for storing temperature, rainfall, and soil characteristics in order to determine which crops are suited for cultivation in a given area. This paper describes a system, implemented as an android application, that employs data analytics techniques to predict the most profitable crop based on current weather and soil conditions. The suggested system will combine data from a repository and the weather department using a machine learning algorithm Using Multiple Linear Regression, it is possible to anticipate the most suited crops based on current environmental circumstances. This gives a farmer a wide range of crops to choose from. As a result, the project creates a system that integrates data from diverse sources, performs data analytics, and conducts predictive analysis in order to improve crop production productivity and boost farmer profit margins over time. Machine learning, crop prediction, and yield estimation are some of the terms used in this paper. Manju D C | Murugan R "Crop Prediction System using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49725.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-processing/49725/crop-prediction-system-using-machine-learning/manju-d-c
Adoption of crop scheduling techniques in India for sugarcane and pomegranate
production has been disappointing. The challenge is to use state of the art technology to provide
practical and useful advice to farmers and further to convince farmers of the benefits of crop
scheduling by on-farm demonstration. The purpose of this project is to describe: 1. To expose the
system to the practical aspects of farming in order to refine it if necessary. 2. To evaluate the
accuracy of the system to predict crop growth and health. 3. A high technology system to provide
practical, real time cropping advice on climate situations. The system consists of a web-based
simulation model that estimates the recent, current and future crop status and yield from field
information and real time weather data. The system automatically generates and distributes simple
advice by SMS to farmers’ cellular phones. The system is evaluated on a small-scale sugarcane and
pomegranate scheme at Pandharpur, Maharashtra. Yields are not affected significantly and
profitability is enhanced considerably.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.