In this study, an investigation was conducted using five prevalent ML algorithms 鈥 Random Forest (RF), XGBoost, Decision Tree (DT), Support Vector Machine (SVM), and Linear Regression (LR) 鈥 on a crop prediction dataset sourced from Kaggle. Algorithms that exhibited the highest...
Computers 12(1), 10 (2023) Article Google Scholar https://www.kaggle.com/datasets/akshatgupta7/crop-yield-in-indian-states-dataset https://www.kaggle.com/datasets/patelris/crop-yield-prediction-dataset Download references Funding The authors declare that no funds, grants, or other support ...
Crop yield prediction is a crucial aspect of agricultural planning and decision-making. This study utilizes a Kaggle dataset featuring State, Year, Season, Crop, Area, Production, etc., employing extensive data preprocessing and one-hot encoding of categorical features to enhance predictive performance...
PlantVillage dataset. Retrieved April 04, 2021, from https://www.kaggle.com/emmarex/plantdisease Google Scholar 7 Image processing in OpenCV. (n.d.). Retrieved April 04, 2021, from https://docs.opencv.org/master/d2/d96/tutorial_py_table_of_contents_imgproc.html Page 15 of 16 Google ...
Firstly, we delve into a crop recommendation dataset obtained from Kaggle, consisting of various input attributes such as the pH of the soil, temperature, humidity, and nutrient levels. Leveraging machine learning classification techniques such as Extra Tree Classifier (ETC), Logistic Regression (LR)...
The dataset for this research is collected from the Kaggle website consisting of 6 different crop types with 11 nutrients. The models are trained and tested with 80% and 20% of the dataset respectively. The results prove Extreme Gradient Boosting followed by Naive Bayes to perform better with ...
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Explore and run machine learning code with Kaggle Notebooks | Using data from Crop Yield Prediction Dataset
Explore and run machine learning code with Kaggle Notebooks | Using data from Crop Yield Prediction Dataset
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