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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | Optimized Placement of Aeroponic Towers using Various Machine Learning Algorithms |
| Authors Name: | Pulipalupula Ramu , D.Koteshwara Rao , NK.Yashwanth , Mrs.M.Nalini |
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IJRTI_211113
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| Published Paper Id: | IJRTI2604052 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | The necessity to have sustainable efficient use of agriculture has raised the demand of the intelligent systems that justify the contemporary practices like aeroponics. The paper suggests Aeroponic Farm Optimization System based on AI, which is a combination of crop suitability prediction and the space allocation optimization, it is aimed at optimizing the use of limited space on the farm. Multi-layer perceptron ANN is applied in the assessment of the environmental conditions within the farm and subsequently assists in the determination of the crop suitability. The model takes into account some of the important parameters such as the type of crop, temperature, humidity, time taken by the sun, the pH of water, the air quality index, and the speed of the wind. In the light of these inputs, the system forecasts whether or not the chosen crop can be developed in the environment it is placed. After the checking of suitability, a hybrid spatial optimization module is used to identify the most optimal location of the aeroponic towers within the farm. It uses a hybrid approach of hexagonal lattice-based geometric packing and metaheuristic optimization techniques like Genetic Algorithm(GA) and Simulated Annealing(SA) to optimize even high tower density and meet the constraint of spacing and boundary. FastAPI and React with Vite were used to create the prototype, involving the use of Fast API as the backend services and React as the user interaction(frontend). It has been experimentally demonstrated that the hybrid optimization strategy has greater tower density than the standalone geometric methods and also has a strict constraint satisfaction. It provides a scalable and smart and efficient solution to aeroponic farm planning, a part of the intelligent agriculture and sustainable food production systems. |
| Keywords: | Aeroponics, Artificial Neural Network, Genetic Algorithm, Simulated Annealing, Smart Agriculture, Optimization. |
| Cite Article: | "Optimized Placement of Aeroponic Towers using Various Machine Learning Algorithms ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a374-a380, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604052.pdf |
| Downloads: | 00080 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604052
Registration ID:211113
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a374-a380 Country: Trichy, Tamilanadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604052 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604052 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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