Integrated ANN and Bidirectional Improved PSO for optimization of fertilizer dose on Palawija plants

Cholissodin, Imam; Dewi, Candra; Surbakti, Eunike Endariahna

doi:10.1109/icsitech.2016.7852632

Cited by 7 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Balanced and optimal use of fertilizer is a part of plant nutrition management as excessive use of it may lower yields, cause damage to the farmland and the environment. Imam et al have optimized the dose of fertilizer by using Integrated Artificial Neural Network (ANN) and Bi-directional Improved Particle Swarm Optimization based on target plant dry weight (ton/ha) and yield (ton/ha) [114]. Optimization of Fertilizer Recommendations in Africa (OFRA), a research project, has developed a fertilizer optimization tool (FOT) for 65 Agro-Ecological Zones (AEZs) and 14 crops of Sub-Saharan Africa to inform farmers about the optimized use of fertilizers [115].…”

Section: Smart Nutrition Managementmentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

View full text Add to dashboard Cite

Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.

show abstract

Section: Smart Nutrition Managementmentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

View full text Add to dashboard Cite

show abstract

“…Dalam penelitian ini penulis menggunakan metode ANN-PSO dan ANN-GA. Dalam penelitian yang membahas optimasi parameter dalam pembuatan genteng pada sebuah pabrik yang menggunakan metode ANN dan PSO oleh Thitipong Navalertporn dan Nitin V. Afzulpurkar (2011) dan optimasi dosis pupuk untuk tanaman jagung dengan ANN dan IPSO oleh Cholissodin (2016) membuktikan bahwa dengan menggunakan kombinasi ANN dan PSO menghasilkan parameterparameter yang optimal sehingga meningkatkan hasil produksi dari 60% menjadi 97% serta mendapatkan hasil yang lebih akurat daripada hanya menggunakan metode backpropagation saja.…”

unclassified

Perbandingan Metode ANN-PSO Dan ANN-GA Dalam Pemodelan Komposisi Pakan Kambing Peranakan Etawa (PE) Untuk Optimasi Kandungan Gizi

Caesar

Hanum

Cholissodin

2016

JTIIK

Self Cite

View full text Add to dashboard Cite

AbstrakSusu merupakan salah satu sumber protein hewani yang mengandung semua zat yang dibutuhkan tubuh. Ternak penghasil susu utama di Indonesia yaitu sapi perah, namun produksi susunya belum dapat mencukupi kebutuhan masyarakat. Alternatifnya adalah kambing peranakan etawa (PE). Tingginya kualitas kandungan gizi susu sangat dipengaruhi oleh beberapa faktor salah satunya, yaitu faktor pakan. Bagian peternakan kambing PE di UPT Pembibitan Ternak dan Hijauan Makanan Ternak Singosari-Malang masih menghadapi permasalahan, yaitu rendahnya kemampuan dalam memberikan komposisi pakan terhadap kambing PE. Kekurangan tersebut berpengaruh terhadap kualitas susu yang dihasilkan. Diperlukan pengetahuan rekayasa kandungan gizi susu untuk menentukan komposisi pakan dalam menghasilkan susu premium dengan kandungan gizi optimal. Penulis membandingkan dua metode yang sudah diteliti yaitu Artificial neural network (ANN) dan Particle Swarm Optimization (PSO) serta metode Artificial Neural Network (ANN) dan Genetic Algorithm (GA) dalam membuat pemodelan pakan kambing dalam mengoptimasi kandungan gizi susu kambing. Dalam analisa pengujian menggunakan metode ANN-PSO yang dilakukan dengan kasus untuk berat badan kambing 36 kg, serta jenis pakan yang digunakan yaitu rumput Odot 70% dan rumput Raja 30%, rata-rata kandungan protein naik 0.707%, sedangkan rata-rata kandungan Lemak turun 0.879%. Dengan menggunakan metode ANN-GA, rata-rata kandungan Protein naik sebesar 0.0852%, sedangkan rata-rata kandungan Lemak turun sebesar 2.3254%.Kata kunci: Susu Kambing, Optimasi, Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Kandungan nutrisi pakan. Abstract Milk is one of the animal protein sources which it contains all of the substances needed by human body. The main milk producer cattle in Indonesia is dairy cow, however its milk production has not fulfilled the society needs. The alternative is the goat, the Etawa crossbreed (PE). The high quality of milk nutrients content is greatly influenced by some factors one of them, is the food factor. The PE goat livestock division of the UPT Cattle Breeding and the Cattle Food Greenery in Singosari-Malang still faces the problem, it is the low ability in giving the

show abstract

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Khan

Taqvi

2022

ChemBioEng Reviews

View full text Add to dashboard Cite

The field of machine learning has proven to be a powerful approach in smart manufacturing and processing in the chemical and process industries. This review provides a systematic overview of current state of artificial intelligence and machine learning and their applications in textile, nuclear power plant, fertilizer, water treatment, and oil and gas industries. Moreover, this study reveals the current dominant machine learning methods, pre and post processing of models, increased utilization of machine learning in terms of fault detection, prediction, optimization, quality control, and maintenance in these sectors. In addition, this review gives the insight into the actual benefits and impact of each method, and complications in their extensive deployment. Finally in the current impressive state, challenges, future development in terms of algorithm and infrastructure aspects are highlighted.

show abstract

Integrated ANN and Bidirectional Improved PSO for optimization of fertilizer dose on Palawija plants

Cited by 7 publications

References 1 publication

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Perbandingan Metode ANN-PSO Dan ANN-GA Dalam Pemodelan Komposisi Pakan Kambing Peranakan Etawa (PE) Untuk Optimasi Kandungan Gizi

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Contact Info

Product

Resources

About