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Title: :  PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES
PaperId: :  23123
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-23123
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
GOKUL KANNAN SPBANNARI AMMAN INSTITUTE OF TECHNOLOGY
SHARMILA ABANNARI AMMAN INSTITUTE OF TECHNOLOGY
ABHINANTHAN SSBANNARI AMMAN INSTITUTE OF TECHNOLOGY
NITHISH KUMAR SBANNARI AMMAN INSTITUTE OF TECHNOLOGY

Abstract

MACHINE LEARNING
Soil Macronutrients, NPK values, KNN, SVM classifier
Everyone has to be healthy and have access to enough crops and food as the world's population rises. The nation's economy benefits from crop output. Accurately estimating the percentage of macronutrients in the soil will assist in selecting the best crop to cultivate. Recent years have seen numerous advancements in everything from harvesting to product selection. The yield is increased when the correct crop is chosen for growing. Understanding the requirements for macronutrients is crucial for achieving optimal yield. The amount of NPK levels needed for various crops varies. The variables that must be taken into account are pH, temperature, humidity, and rainfall. Different machine learning models are often trained, tested, and validated. Prediction techniques include Decision Trees, AdaBoost Classifiers, XGB Classifiers, Random Forests, Logistic Regressions, SVM (Support Vector Machine) Classifiers, and KNN Algorithms. We can select the most effective model by contrasting the accuracy of several models. The creation of a user-friendly website is the suggested process. Inputs from the user include NPK levels, pH, temperature, humidity, and rainfall. Following the machine learning model's study, the website will take the macronutrients values as the input and suggest us the appropriate crop that may be cultivated in a given area as an output. We saw improved accuracy in the KNN Algorithm, SVM Classifier, and logistic regression during training, testing, and validation. The KNN algorithm's accuracy was determined to be 0.9886. We have used Google colab for training and testing the machine with datasets. We have created a user-friendly website by merging these two procedures.

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IJARIIE GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, and NITHISH KUMAR S. "PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 2682-2689
MLA GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, and NITHISH KUMAR S. "PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 2682-2689.
APA GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, & NITHISH KUMAR S. (2024). PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 2682-2689.
Chicago GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, and NITHISH KUMAR S. "PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 2682-2689.
Oxford GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, and NITHISH KUMAR S. 'PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 2682-2689. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/PREDICTION_AND_ANALYSIS_OF_SOIL_MACRONUTRIENTS_USING_MACHINE_LEARNING_TECHNIQUES_ijariie23123.pdf (Accessed : ).
Harvard GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, and NITHISH KUMAR S. (2024) 'PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 2682-2689IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/PREDICTION_AND_ANALYSIS_OF_SOIL_MACRONUTRIENTS_USING_MACHINE_LEARNING_TECHNIQUES_ijariie23123.pdf (Accessed : )
IEEE GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, and NITHISH KUMAR S, "PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 2682-2689, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/PREDICTION_AND_ANALYSIS_OF_SOIL_MACRONUTRIENTS_USING_MACHINE_LEARNING_TECHNIQUES_ijariie23123.pdf [Accessed : ].
Turabian GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, and NITHISH KUMAR S. "PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver GOKUL KANNAN SP, SHARMILA A, ABHINANTHAN SS, and NITHISH KUMAR S. PREDICTION AND ANALYSIS OF SOIL MACRONUTRIENTS USING MACHINE LEARNING TECHNIQUES. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 2682-2689. Available from: https://ijariie.com/AdminUploadPdf/PREDICTION_AND_ANALYSIS_OF_SOIL_MACRONUTRIENTS_USING_MACHINE_LEARNING_TECHNIQUES_ijariie23123.pdf
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