Improved fuzzy ant colony optimization to recommend cultivation in Tamil Nadu, India

• Autorzy: Ezhilarasi Thamarai Pugazhendhi , Rekha Karthikeyan Sashi

Identyfikatory

DOI

10.1007/s11600-022-00823-6

• Języki publikacji: EN

Abstrakty

EN

The crop recommendation of the rural farmers is important in our country; this research work aim is to increase the profit of the farmers by suggesting the suitable crop recommendation in towns and villages of Tamil Nadu. The agriculture sectors are widespread that requires thorough preparation and judgement. Artificial intelligence and machine learning algorithms are extended practically in every major area, including agriculture. Data on Tamil Nadu’s agricultural production were obtained through an open data platform and also from the manual of the Economic and Statistical Department of Tamil Nadu which is published each year. Their main objective was to collect knowledge through data that could be applied to obtain useful predictable results. Hence, to achieve these objectives, fuzzy ant clustering with detection of cluster similarity and cluster combination along with association rule mining is used to provide crop recommendation to farmers depending on the current season and soil type. By evaluating the previous year’s agriculture production record, analyse the yield produced in the previous year by various crops and seasons. An algorithm using fuzzy ant clustering with detecting and combining the overlapping nodes to reduce the redundancy and improve the quality of the clusters was developed. The evaluation results show that the fuzzy ant colony with overlapping cluster detection algorithm provides good RS of the crops as the error rate is decreased to 8 percentage and accuracy is increased to 91.9 percentage when compared with results obtained from crop recommendation system with ant colony clustering and association rule mining.

Słowa kluczowe

EN

agricultural; knowledge extraction algorithm; intelligence system; collaboration filtration; suggestion frameworks; solid earth sciences

PL

rolnictwo; algorytm ekstrakcji wiedzy; inteligentny system

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2873--2887
• Opis fizyczny: Bibliogr. 25 poz.

Twórcy

autor

Ezhilarasi Thamarai Pugazhendhi

• Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha University, Chennai, India

autor

Rekha Karthikeyan Sashi

• Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha University, Chennai, India

Bibliografia

• 1. Amarapur B (2017) An automated approach for brain tumor identification using ANN classifier. In: 2017 International conference on current trends in computer, electrical, electronics and communication (CTCEEC), pp 1011–1016. IEEE, New York
• 2. Ambika, Rajkumar L. Biradar (2021) A robust low frequency integer wavelet transform based fractal encryption algorithm for image steganography. Int J Adv Intell Paradigms 19(3–4):342–356
• 3. Banerjee G, Sarkar U, Ghosh I (2021) A fuzzy logic-based crop recommendation system. In: Proceedings of international conference on frontiers in computing and systems, pp 57–69. Springer, Singapore.
• 4. Belkhier Y, Achour A (2020) Fuzzy passivity-based linear feedback current controller approach for PMSG-based tidal turbine. Ocean Eng 218:108156
• 5. Botega LC, Cruvinel PE (2020) Sensors-based virtual reality environment for volumetric CT analyses of agricultural soils samples. In: Proceedings of the IARIA, ALLSENSORS 2020: the fifth international conference on advances in sensors, actuators, metering and sensing, pp 27–34.
• 6. Boursianis AD, Papadopoulou MS, Diamantoulakis P, Liopa-Tsakalidi A, Barouchas P, Salahas G, Karagiannidi G, Wan S, Goudos SK (2020) Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things, 100187.
• 7. Brambilla M, Romano E, Toscano P, Cutini M, Biocca M, Ferré C, Bisaglia C (2021) From conventional to precision fertilization: a case study on the transition for a small-medium farm. AgriEngineering 3(2):438–446
• 8. Brijs J, Føre M, Gräns A, Clark TD, Axelsson M, Johansen JL (2021) Bio-sensing technologies in aquaculture: how remote monitoring can bring us closer to our farm animals. Philos Trans R Soc B 376(1830):20200218
• 9. Demestichas K, Daskalakis E (2020) Data lifecycle management in precision agriculture supported by information and communication technology. Agronomy 10(11):1648
• 10. Dhabal G, Lachure J, Doriya R (2021) Crop recommendation system with cloud computing. 2021 third international conference on inventive research in computing applications (ICIRCA). IEEE, New York, pp 1404–1411
• 11. Garanayak M, Sahu G, Mohanty SN, Jagadev AK (2021) Agricultural recommendation system for crops using different machine learning regression methods. Int J Agric Environ Inform Syst (IJAEIS) 12(1):1–20
• 12. Gu Q, Grogan P (2020) Nutrient availability measurement techniques in arctic tundra soils: in situ ion exchange membranes compared to direct extraction. Plant Soil 454(1):359–378
• 13. Hao J, Huang F, Cheng D, Mu S, Li L (2020) Performance of snow density measurement systems in snow stratigraphies. Geosci Instrum Methods Data Syst Discuss
• 14. Hartono A, Nadalia D, Sulaeman D (2021) Development of quick test method for soil pH, nitrat, phosphorus, and potassium combining chemicals and phone cellular application. AGRIVITA J Agric Sci 43(2):371–384
• 15. Iqbal J, Xu R, Halloran H, Li C (2020) Development of a multi-purpose autonomous differential drive mobile robot for plant phenotyping and soil sensing. Electronics 9(9):1550
• 16. Josephson C, Kotaru M, Winstein K, Katti S, Chandra R (2021) Low-cost In-ground Soil Moisture Sensing with Radar Backscatter Tags. In: ACM SIGCAS conference on computing and sustainable societies, pp 299–311.
• 17. Kashyap B, Kumar R (2021) Sensing methodologies in agriculture for soil moisture and nutrient monitoring. IEEE Access 9:14095–14121
• 18. Madhuri J, Indiramma M (2021) Artificial neural networks based integrated crop recommendation system using soil and climatic parameters. Indian J Sci Technol 14(19):1587–1597
• 19. Mancini M, Silva SHG, dos Santos Teixeira AF, Guilherme LRG, Curi N (2020) Soil parent material prediction for Brazil via proximal soil sensing. Geoderma Reg 22:e00310
• 20. Priyadharshini A, Chakraborty S, Kumar A, Pooniwala OR (2021) Intelligent crop recommendation system using machine learning. In: 2021 5th international conference on computing methodologies and communication (ICCMC), pp 843–848. IEEE, New York
• 21. Rangayya V, Patil N (2021) Facial image segmentation by integration of level set and neural network optimization with hybrid filter pre-processing model. Eng Sci 16:1–10
• 22. Uplaonkar DS, Patil N (2021) An efficient discrete wavelet transform based partial hadamard feature extraction and hybrid neural network based monarch butterfly optimization for liver tumor classification. Eng Sci 16:354–365
• 23. Veerashetty S (2021) Multi-modal weighted denoising coder for the management of lost information in healthcare big data. Int J Innovations Sci Eng Res 8(5):141–148
• 24. Virupakshappa A (2021) Diagnosis of melanoma with region and contour based feature extraction and KNN classification. Int J Innovations Sci Eng Res 8(5):157–164
• 25. Virupakshappa AB (2018) A segmentation approach using level set coding for region detection in MRI images. Computational signal processing and analysis. Lecture Notes in Electrical Engineering, Springer, Singapore, p 490.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).