1
Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India.
(rittikkaadhav@gmail.com)
2
Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India.
(akhilaaynikkadan@gmail.com)
3
Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India
(dhunalvaishnavi05@gmail.com)
4
Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India
(sakshi.karade16@gmail.com)
5
Computer Engineering, Sanjivani College of Engineering, Kopargoan, Savitribai Phule Pune University, India.
(Vengicse2005@gmail.com)
Abstract :
The main purpose of the planned system is to develop a system which can intelligently recommend crop and suggest required measures to farmers for their profitable income. For this purpose, machine learning algorithms are used. The main goal of these systems is to achieve maximum yield rate of crop using land resource. In this system, the farmer / beginner will classify and predict the crop cultivation based on their weather, monsoon and soil type along with their pH level. Forclassification we have used the K-Means algorithm for the choosing crop, the cultivation process is recommended in the form of text. During the cultivation of crops, the fertilizers, insecticides and fungicides are recommended using Machine Learning Technique. The system also predicts the name of disease and its remedies if leaf of crop is diseased .Finally, using this system the farmers will have a well guided approach to begin with farming.The pandemic has affected a lot of fields around the world. One of them is the agricultural sector. Many farmers in the urban as well as the rural parts of India were not able to earn their profile in spite of having good production. This system would eliminate these worries. All the information needed can be accessed online.
Keywords :
Machine learning; Deep learning; crop recommendation; fertilizer recommendation; plant disease; Naïve Bayes; image processing; classification; K-means.
References :
[1] Sonal Jain, Dharavath Ramesh,” Machine Learning convergence for weather based crop selection”, IEEE
International Students’ Conference on Electrical, Electronics and Computer Science(2020)
[2] Dr.V.D.Ambeth Kumar, Dr.M.Ramakrishnan, V.D.Ashok Kumar and Dr.S.Malathi (2015) “Performance
Improvement using an Automation System for Recognition of Multiple Parametric Features based on Human
Footprint” for the International Journal of kuwait journal of science & engineering, Vol 42, No 1 (2015),
pp:109-132. https://doi.org/10.5370/JEET.2015.10.4.1815
[3] ShubhamPawar, SumitDere, AshitoshAkangire,HarshvardhanKamble, Prof. SeemaShrawne ,” Smart Farming
Using Machine Learning”, researchgate.net/publication/351692492 (2021)
[4]Ankit Kumar, Abhishek Kumar, Ali Kashif Bashir, Mamoon Rashid, V. D. Ambeth Kumar, RupakKharel,
“Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset”, ACM
Transactions on Management Information Systems, Vol. 13, No. 1, Article 8. Publication date: October 2021
https://doi.org/10.1145/3469891
[5]C.P.Wickramasinghe ,P.L.N.Lakshitha, H.P.H.S.Hemapriya , AnuradhaJayakody, P.G.N.S.Ranasinghe , “ Smart
Crop and Fertilizer Prediction System ”, International Conference on Advancements in Computing (ICAC)
(2019)
[6] Ambeth Kumar S. Malathi R. Venkatesan K Ramalakshmi, Weiping Ding, Abhishek Kumar “Exploration of an
innovative geometric parameter based on performance enhancement for foot print recognition”, Journal of
Intelligent and Fuzzy System , vol. 38, no. 2, pp. 2181-2196, 2020. DOI: 10.3233/JIFS-190982
[7] Pushkara Sharma, Pankaj Hans, Subhash Chand Gupta,”Classification of Plant Leaf Diseases Using MAchine
Learning and Image Preprocessing Techniques.”,978-1-7281-2791-0/20/IEEE (2020)
[8] PoojanPanchal, VigneshCharanRaman,”Plant Diseases Detection and Classification using MAchine Learning
Models.”,978-1-7281-2619-7/19 IEEE (2019).
| Style | # |
|---|---|
| MLA | Ritika Adhav, AghilaUnnikrishnan, VaishnaviDhumal, SakshiKarade, K.Vengatesan. "Survey on Crop Recommendation System." Journal of Cognitive Human-Computer Interaction, Vol. 3, No. 2, 2022 ,PP. 21-25 (Doi : https://doi.org/10.54216/JCHCI.030203) |
| APA | Ritika Adhav, AghilaUnnikrishnan, VaishnaviDhumal, SakshiKarade, K.Vengatesan. (2022). Survey on Crop Recommendation System. Journal of Journal of Cognitive Human-Computer Interaction, 3 ( 2 ), 21-25 (Doi : https://doi.org/10.54216/JCHCI.030203) |
| Chicago | Ritika Adhav, AghilaUnnikrishnan, VaishnaviDhumal, SakshiKarade, K.Vengatesan. "Survey on Crop Recommendation System." Journal of Journal of Cognitive Human-Computer Interaction, 3 no. 2 (2022): 21-25 (Doi : https://doi.org/10.54216/JCHCI.030203) |
| Harvard | Ritika Adhav, AghilaUnnikrishnan, VaishnaviDhumal, SakshiKarade, K.Vengatesan. (2022). Survey on Crop Recommendation System. Journal of Journal of Cognitive Human-Computer Interaction, 3 ( 2 ), 21-25 (Doi : https://doi.org/10.54216/JCHCI.030203) |
| Vancouver | Ritika Adhav, AghilaUnnikrishnan, VaishnaviDhumal, SakshiKarade, K.Vengatesan. Survey on Crop Recommendation System. Journal of Journal of Cognitive Human-Computer Interaction, (2022); 3 ( 2 ): 21-25 (Doi : https://doi.org/10.54216/JCHCI.030203) |
| IEEE | Ritika Adhav, AghilaUnnikrishnan, VaishnaviDhumal, SakshiKarade, K.Vengatesan, Survey on Crop Recommendation System, Journal of Journal of Cognitive Human-Computer Interaction, Vol. 3 , No. 2 , (2022) : 21-25 (Doi : https://doi.org/10.54216/JCHCI.030203) |