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.
When we think of agriculture we think of cultivation,
plant life, soil fertility, types of crops, terrestrial environment,
etc. But in today’s world we associate with agriculture terms
like climate change, irrigation facilities, technological
advancements, synthetic seeds, advanced machinery etc. In
short we are interested in how science of today can help us in
the field of agriculture. And so comes into the picture
Precision Agriculture (PA).
The general definition is information and technology
based farm management system to identify, analyze and
manage spatial and temporal variability within fields for
optimum productivity and profitability, sustainability and
protection of the land resource by minimizing the production
costs. Simply put, precision farming is an approach where
inputs are utilized in precise amounts to get increased average
yields compared to traditional cultivation techniques. Hence it
is a comprehensive system designed to optimize production
with minimal adverse impact on our terrestrial system. [1]
The three major components of precision agriculture
are information, technology and management. Precision
farming is information-intense. Precision Agriculture is a
management strategy that uses information technologies to
collect valuable data from multiple sources. This type of analyzing data gives idea what to do in upcoming years to tackle the situations.
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.
When we think of agriculture we think of cultivation,
plant life, soil fertility, types of crops, terrestrial environment,
etc. But in today’s world we associate with agriculture terms
like climate change, irrigation facilities, technological
advancements, synthetic seeds, advanced machinery etc. In
short we are interested in how science of today can help us in
the field of agriculture. And so comes into the picture
Precision Agriculture (PA).
The general definition is information and technology
based farm management system to identify, analyze and
manage spatial and temporal variability within fields for
optimum productivity and profitability, sustainability and
protection of the land resource by minimizing the production
costs. Simply put, precision farming is an approach where
inputs are utilized in precise amounts to get increased average
yields compared to traditional cultivation techniques. Hence it
is a comprehensive system designed to optimize production
with minimal adverse impact on our terrestrial system. [1]
The three major components of precision agriculture
are information, technology and management. Precision
farming is information-intense. Precision Agriculture is a
management strategy that uses information technologies to
collect valuable data from multiple sources. This type of analyzing data gives idea what to do in upcoming years to tackle the situations.
Precision Agriculture: a concise introduction Joseph Dwumoh
The presentation supplies a brief introduction to what precision agriculture is, what drives its adoption, and what challenges the acceptance of the technologies involved.
Commercial & research landscape for smart irrigation systems. A survey of commercial product offerings, research prototypes and approaches to smart irrigation. I also cover the why there is such a dire need to conserve water and increase yield.
results of FieldFact project (EU FP6) concerning relevant EGNOS precision based applications for European agriculture. Three applications show how EGNOS and precision agriculture are critical instruments in transforming agriculture into a sustainable sector.
Agricultural greenhouse gas calculators overestimate fluxes in tropical farming systems
Poster presented by Meryl Breton Richards at the 3rd Global Science Conference on Climate-Smart Agriculture in Montpellier.
Read more: http://ccafs.cgiar.org/3rd-global-science-conference-%E2%80%9Cclimate-smart-agriculture-2015%E2%80%9D#.VRurLUesXX4
SENSOR BASED SMART IRRIGATION SYSTEM WITH MONITORING AND CONTROLLING USING IN...ijasa
This paper presents the development of a sensor based smart irrigation system with the capabilities of remote monitoring and controlling of water usage in the agriculture field using Internet of Things (IoT). With the employment of IoT in irrigation system, all agricultural information can be viewed and controlled at the user's fingertips. The system consists of a microcontroller (Node MCU), sensors (soil moisture, DHT11), and irrigation of a water pump with a decision-making system. Sensors are linked to a Wi-Fi module (Node MCU) and are interdependent to provide increased sensitivity to the irrigation system. The data obtained will be uploaded to the cloud (ThingSpeak) and presented in the form of graphs accessible via the website. A web page is used to control the water pump for irrigation purposes. This paper is managed to meet all of its aims to help farmers in terms of time, project cost, labor, water consumption, power consumption, and reliability by implementing the IoT-based smart irrigation system.
Precision Agriculture: a concise introduction Joseph Dwumoh
The presentation supplies a brief introduction to what precision agriculture is, what drives its adoption, and what challenges the acceptance of the technologies involved.
Commercial & research landscape for smart irrigation systems. A survey of commercial product offerings, research prototypes and approaches to smart irrigation. I also cover the why there is such a dire need to conserve water and increase yield.
results of FieldFact project (EU FP6) concerning relevant EGNOS precision based applications for European agriculture. Three applications show how EGNOS and precision agriculture are critical instruments in transforming agriculture into a sustainable sector.
Agricultural greenhouse gas calculators overestimate fluxes in tropical farming systems
Poster presented by Meryl Breton Richards at the 3rd Global Science Conference on Climate-Smart Agriculture in Montpellier.
Read more: http://ccafs.cgiar.org/3rd-global-science-conference-%E2%80%9Cclimate-smart-agriculture-2015%E2%80%9D#.VRurLUesXX4
SENSOR BASED SMART IRRIGATION SYSTEM WITH MONITORING AND CONTROLLING USING IN...ijasa
This paper presents the development of a sensor based smart irrigation system with the capabilities of remote monitoring and controlling of water usage in the agriculture field using Internet of Things (IoT). With the employment of IoT in irrigation system, all agricultural information can be viewed and controlled at the user's fingertips. The system consists of a microcontroller (Node MCU), sensors (soil moisture, DHT11), and irrigation of a water pump with a decision-making system. Sensors are linked to a Wi-Fi module (Node MCU) and are interdependent to provide increased sensitivity to the irrigation system. The data obtained will be uploaded to the cloud (ThingSpeak) and presented in the form of graphs accessible via the website. A web page is used to control the water pump for irrigation purposes. This paper is managed to meet all of its aims to help farmers in terms of time, project cost, labor, water consumption, power consumption, and reliability by implementing the IoT-based smart irrigation system.
Accurate prediction of crop yield is very difficult.because it depends on nature which is chane day by day. The historical data is insufficient and inevident due to the naturo climatical changes . The poverty of people's thirst still unbalanced. To avoid the drawback of existing manual prediction, use deep learning methods to predict the future crops more accurately during this uneven climate. The soil, weather and climate features are taken as a parameters. In past few years machine learning algorithms used to predict the future forecast. Here deep neural network is introduced to get more accurate data than other networks. The online gradient descent used to find the online learning deep neural network depth. It automatically decides the depth of the neural network
This is about survey the crop yield prediction using some data mining classification methods namely prdiction with classification,residue climate control, feature selection extraction, crop classification models,evaluation metrics, accuracy level,classification decision, result analysis,rain fall pH, principal component analysis, information gain
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
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.
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.
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.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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
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.
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.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.