Agricultural Analysis of Machine Learning Algorithms for Crop Prediction

Agricultural Analysis of Machine Learning Algorithms for Crop Prediction

Farming holds great importance in India, a country that depends significantly on agriculture. According on the 2022-23 census, the proportion of Gross Value Added (GVA) contributed by agricultural and associated sectors to the overall Indian economy is 18.3%. When choosing which crop to cultivate an...

Full description

Saved in:
Bibliographic Details
Published in:2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) pp. 1196 - 1203
Main Authors: Savaliya, Laksh, Sapovadiya, Manav, Garg, Dweepna, Patel, Premal, Shah, Milind
Format: Conference Proceeding
Language:English
Published: IEEE 03-10-2024
Subjects:
Accuracy
Agriculture
Analysis
Crop Prediction
K-mean
Machine Learning
Programming
Python
Random Forest
Random forests
Recommender systems
Seeds (agriculture)
Soil
Support vector machines
SVM
Temperature
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Farming holds great importance in India, a country that depends significantly on agriculture. According on the 2022-23 census, the proportion of Gross Value Added (GVA) contributed by agricultural and associated sectors to the overall Indian economy is 18.3%. When choosing which crop to cultivate and farm, it is important to examine several factors such as production rate, environmental conditions, soil type, temperature and pH .The project addresses the critical need for accessible information in agriculture, revolutionizing farmers decision-making with personalized crop management recommendations. Also, the proposed solution to this problem involves the use of an ensemble model with a majority voting technique. The model utilizes K-Mean, Random Forest, Linear Regression, and Support Vector Machine as learners to recommend a crop based on site-specific parameters. This approach ensures high accuracy and efficiency in the crop recommendation system. The hybrid method combining K-means and random Forest achieved the best accuracy of 99.77% compared to other algorithms. Hence, the proposed approach will assist farmers in selecting the appropriate seed and identifying plants based on soil specifications, thereby enhancing crop yield. Maximize efficiency and generate financial gain from this approach. Python is used for programming, and the libraries Pandas, NumPy, Scikit-Learn, Matplotlib, and Seaborn are employed.
ISSN:2768-0673
DOI:10.1109/I-SMAC61858.2024.10714828