Development of Artificial Intelligence for Variable Rate Application Based Oil Palm Fertilization Recommendation System

2021 ◽  
Author(s):  
Erick Firmansyah ◽  
Bens Pardamean ◽  
Candra Ginting ◽  
Hangger Gahara Mawandha ◽  
Dian Pratama Putra ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Oil Palm ◽  
Recommendation System ◽  
Variable Rate ◽  
Variable Rate Application

Evaluation of Centrifugal Spreader Response to Variable Rate Application by Using Task File Data

2020 ◽  
Vol 53 (2) ◽  
pp. 15804-15809
Author(s):  
Galibjon M. Sharipov ◽  
Andreas Heiß ◽  
Hans W. Griepentrog ◽  
Dimitrios S. Paraforos
Keyword(s):  
Variable Rate ◽  
Variable Rate Application

The sensitivity of economic gains from high-speed planting

2017 ◽  
Vol 8 (2) ◽  
pp. 662-667
Author(s):  
C. R. Dillon ◽  
J. Shockley ◽  
T. Mark
Keyword(s):  
Optimization Model ◽  
Technological Progress ◽  
High Speed ◽  
Operating Cost ◽  
Cost Savings ◽  
Variable Rate ◽  
Net Returns ◽  
Variable Rate Application ◽  
Technology Changes ◽  
Profit Potential

Recent technological progress in high-speed planting (HSP) warrants economic analysis of its potential. A whole farm optimization model of a 1000 ha Kentucky, USA corn and soybean operation finds that operating cost savings (labor, fuel, tractor repairs) and yield increases couple in recovering annual ownership costs of HSP technology. Changes in farm net returns are positive for all 12-row planter scenarios and all double speed cases for the 16-row planter but not for a 50% increase in speed with the 16-row planter. The greatest profit potential occurred when adopting the combination of HSP and variable rate application (VRA), with increased net returns of up to 6.57% compared to conventional speed no VRA for the 12-row planter.

scholarly journals The algorithm and the flow: Netflix, machine learning and recommendation algorithms

Intexto ◽  
2019 ◽  
pp. 166-184
Author(s):  
João Damasceno Martins Ladeira
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recommendation System ◽  
Cultural Impact ◽  
Recommendation Algorithms ◽  
Definition Of

This article discusses the Netflix recommendation system, expecting to understand these techniques as a part of the contemporary strategies for the reorganization of television and audiovisual. It renders problematic a technology indispensable to these suggestions: the tools for artificial intelligence, expecting to infer questions of cultural impact inscribed in this technique. These recommendations will be analyzed in their relationship with the formerly decisive form for the constitution of broadcast: the television flow. The text investigates the meaning such influential tools at the definition of a television based on the manipulation of collections, and not in the predetermined programming, decided previously to the transmission of content. The conclusion explores the consequences of these archives, which concedes to the user a sensation of choice in tension with the mechanical character of those images.

Variable-rate application technologies in precision agriculture

2018 ◽  
pp. 171-193
Author(s):  
Kenneth A. Sudduth ◽  
◽  
Aaron J. Franzen ◽  
Heping Zhu ◽  
Scott T. Drummond ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Variable Rate ◽  
Variable Rate Application

Library book intelligent recommendation system based on artificial intelligence

2021 ◽  
pp. 1-6
Author(s):  
Lu Pang
Keyword(s):  
Artificial Intelligence ◽  
Recommendation System ◽  
Absolute Error ◽  
Recall Rate ◽  
Data Set ◽  
Evaluation Data ◽  
Artificial Intelligence Technology ◽  
Collaborative Filtering Algorithm ◽  
Book Evaluation ◽  
Evaluation Matrix

In order to improve the accuracy of intelligent recommendation of library books, an intelligent recommendation system of library books based on artificial intelligence was designed. The system uses artificial intelligence technology to clean up and normalize the data, automatically extracts the user’s historical evaluation data of books, divides the whole user space into several similar user clusters through the similar user clustering module, constructs the user book evaluation matrix according to the historical evaluation data, and uses the hybrid collaborative filtering algorithm which integrates user based and project-based to predict each user a book evaluation matrix of similar user clusters was used to realize the intelligent recommendation of library books, and the recommendation results were displayed to users through the user interface module. The results show that the average absolute error and root mean square error of the system are always the lowest, and the recommendation accuracy is high. When the control parameter is 0.4, the best intelligent book recommendation effect can be obtained; the recommended recall rate is not affected by the sparse density of the data set, and the stability is strong.

A Movie E-Shop Recommendation Model Based on Web Usage and Ontological Data

2011 ◽  
pp. 51-68
Author(s):  
Andreas Aresti ◽  
Penelope Markellou ◽  
Ioanna Mousourouli ◽  
Spiros Sirmakessis ◽  
Athanasios Tsakalidis
Keyword(s):  
Artificial Intelligence ◽  
User Modeling ◽  
Web Mining ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Model Based ◽  
Web Usage ◽  
One To One ◽  
Online Shops

Recommendation systems are special personalization tools that help users to find interesting information and services in complex online shops. Even though today’s e-commerce environments have drastically evolved and now incorporate techniques from other domains and application areas such as Web mining, semantics, artificial intelligence, user modeling, and profiling setting up a successful recommendation system is not a trivial or straightforward task. This chapter argues that by monitoring, analyzing, and understanding the behavior of customers, their demographics, opinions, preferences, and history, as well as taking into consideration the specific e-shop ontology and by applying Web mining techniques, the effectiveness of produced recommendations can be significantly improved. In this way, the e-shop may upgrade users’ interaction, increase its usability, convert users to buyers, retain current customers, and establish long-term and loyal one-to-one relationships.

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