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 proposed method is trained and tested with publicly available Kaggle datasets and real-time collected Maharashtra datasets consisting of both soil and climatic parameters. Initially, data from the dataset are collected and fed into a new Concurrent Excited Gated Recurrent Unit (CEGRU) based DL ...
Agriculture is the backbone of the Indian economy; more over half of the world’s people depend on rice production. Environmental conditions, quality
This dataset contains 1300 images of sesame crops and different types of weeds with each image labels. Each image is a 512 X 512 color image. Labels for images are in YOLO format. Data on https://www.kaggle.com/ravirajsinh45/crop-and-weed-detection-data-with-bounding-boxes...
Proposed Model 2023 Kaggle dataset + own dataset Yes CatBoost Accuracy: 97.5 %, F1 Score: 97.5 % 4.4.2. Fertilizer prediction After several iterations and fine-tuning, we successfully developed a fertilizer prediction model using the Random Forest algorithm combined with grid searchCV. The hyperpar...
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 ...
The proposed OntoFusionCrop uses ontology cluster that includes soil ontology, crop ontology, geographical ontology and agricultural ontology. This strategy uses the crop recommendation dataset from Kaggle which is classified using bagging. The classified instances along with the ontology cluster are ...
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...
Additionally, the dataset can be used to train machine learning models to predict crop yields and production in different parts of the country, which can be valuable for agricultural businesses and organizations. Overall, the dataset provides a comprehensive overview of crop production statistics in ...
This dataset encompasses extensive information on crop production in India, spanning multiple years and offering insights into agricultural trends and patterns. The dataset consists of over 246,000 records, capturing a wide array of variables related to crop production, and is intended to facilitate ad...