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Title: :  Crop prediction and fertilizer reccomendation
PaperId: :  23160
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-23160
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Mr.R.S.Kakade Padmashri Dr Vitthalrao Vikhe Patil Institute of Technology and engineering (polytechnic
Aryan Jagzap Padmashri Dr Vitthalrao Vikhe Patil Institute of Technology and engineering (polytechnic)
priya pune Padmashri Dr Vitthalrao Vikhe Patil Institute of Technology and engineering (polytechnic)
anjali gadekar Padmashri Dr Vitthalrao Vikhe Patil Institute of Technology and engineering (polytechnic)
Aarti dahatonde Padmashri Dr Vitthalrao Vikhe Patil Institute of Technology and engineering (polytechnic)

Abstract

computer engineering
Precision Agriculture crop recommendation system, crop disease prediction, Internet of Things, Machine Learning
Agriculture plays a vital role in India. India is the world's largest producer of different crops but still, it uses traditional framing methods therefore crop yield becomes down. Hence, with the introduction of newer seed varieties, new methods of agriculture crop production have increased. But without using the smarter ways, the agricultural field still having an imperfection. And due to these farmers need a smarter way to increase crop production. Hence, to maximize the crop yield some smart methods came into the picture used in IoT and Machine Learning. In this paper, we will review the algorithms like Random Forest, Decision Tree, ANN to get better accuracy for the system. The core components of this system include a network of IoT sensors and actuators, data analytics, machine learning algorithms, and a user-friendly interface. IoT sensors are strategically deployed in the fields to continuously measure crucial parameters such as soil moisture, temperature, humidity, and light intensity. This real-time data is transmitted to a centralized database, which is then processed and analyzed using advanced machine learning algorithms. The system utilizes machine learning models to provide accurate crop yield predictions based on historical data, current conditions, and crop-specific characteristics. By considering factors like soil quality, weather conditions, and crop type, the system generates optimal fertilizer recommendations. These recommendations aim to optimize nutrient supply, reduce over-fertilization, and minimize environmental impact. Farmers and agricultural professionals can access the system through a user-friendly interface, such as a mobile application or web platform. Here, they can receive personalized recommendations for fertilization and access real-time data on their crops' health and environmental conditions. This not only enhances crop yield but also streamlines decision-making processes for farmers, reducing costs and environmental harm.

Citations

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IJARIIE Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, and Aarti dahatonde. "Crop prediction and fertilizer reccomendation" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 2915-2920
MLA Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, and Aarti dahatonde. "Crop prediction and fertilizer reccomendation." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 2915-2920.
APA Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, & Aarti dahatonde. (2024). Crop prediction and fertilizer reccomendation. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 2915-2920.
Chicago Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, and Aarti dahatonde. "Crop prediction and fertilizer reccomendation." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 2915-2920.
Oxford Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, and Aarti dahatonde. 'Crop prediction and fertilizer reccomendation', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 2915-2920. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Crop_prediction_and_fertilizer_reccomendation_ijariie23160.pdf (Accessed : 16 September 2024).
Harvard Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, and Aarti dahatonde. (2024) 'Crop prediction and fertilizer reccomendation', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 2915-2920IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Crop_prediction_and_fertilizer_reccomendation_ijariie23160.pdf (Accessed : 16 September 2024)
IEEE Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, and Aarti dahatonde, "Crop prediction and fertilizer reccomendation," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 2915-2920, Mar-App 2024. [Online]. Available: http://ijariie.com/AdminUploadPdf/Crop_prediction_and_fertilizer_reccomendation_ijariie23160.pdf [Accessed : 16 September 2024].
Turabian Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, and Aarti dahatonde. "Crop prediction and fertilizer reccomendation." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 (16 September 2024).
Vancouver Mr.R.S.Kakade, Aryan Jagzap, priya pune, anjali gadekar, and Aarti dahatonde. Crop prediction and fertilizer reccomendation. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 16 September 2024]; 10(2) : 2915-2920. Available from: http://ijariie.com/AdminUploadPdf/Crop_prediction_and_fertilizer_reccomendation_ijariie23160.pdf
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