Crop Selection Dataset, Predicting Crop Selection Based on Soil,
Crop Selection Dataset, Predicting Crop Selection Based on Soil, Climate, and Nutrient Requirements Tailored Crop Recommendations for Success. Agriculture is the fundamental of India’s economy, and using the concept of Data Driven Farming presenting an important opportunity to boost national productivity. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Sep 4, 2024 · The dataset is comprehensive, encompassing various key factors critical to machine learning-based crop recommendation systems. By analyzing extensive datasets, these algorithms can predict the best cultivation practices, reducing resource wastage and improving efficiency. This repository contains a newly developed global dataset of climate-responsive crop varieties and sowing dates for six major crops: maize, soybean, winter wheat, spring wheat, rice (season 1), and rice (season 2). So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. It enables informed decisions to optimize crop yield, resource management May 11, 2023 · For crop yield prediction, this study includes parameters such as temperature, rainfall, area, humidity, soil nutrients, pH, and AUC (Area under Cultivation). Classification Problem dataset Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. These findings can contribute to developing improved crop selection systems by proposing suitable techniques and identifying crucial parameters. Flexible Data Ingestion. The paper proposes a We’re on a journey to advance and democratize artificial intelligence through open source and open science. The integration of Machine Learning not only provides a data-driven approach to crop selection but also promotes sustainable agriculture, contributing to food security and environmental conservation. This dataset is ideal for building machine learning classification models that predict the optimal crop type based on inputs such as soil pH, nitrogen, phosphorus, potassium, rainfall, temperature, and humidity. In Jharkhand more than 80% of the Crop yields of Indian States and UTs from year 1997-2020 Download scientific diagram | Feature wise exploration on crop selection dataset from publication: Selection of crop varieties and yield prediction based on phenotype applying deep learning | div May 11, 2023 · For crop yield prediction, this study includes parameters such as temperature, rainfall, area, humidity, soil nutrients, pH, and AUC (Area under Cultivation). It contains columns representing the amount of fertilizer used, temperature, and soil nutrients (nitrogen, phosphorus, and potassium), along with the corresponding crop yield. ** Dec 1, 2024 · This paper predicted future crop demand using available datasets, including crop areas, types, soil characteristics, yields, and consumption. By utilizing machine learning algorithms and extensive datasets, this system provides actionable insights tailored to specific environmental conditions, thereby enhancing agricultural productivity and Download scientific diagram | Analysis of dataset with respect to crops (Training data) from publication: Crop recommendation system for precision agriculture | Precision Agriculture and Crop A crop recommendation system helps farmers select the best crops to grow based on the specific properties of their soil. This precision agriculture approach enhances resource efficiency, reduces input costs, and mitigates environmental impact. Comparison and Selection of ML Algorithm We first must assess and compare different algorithms before selecting the one that best matches this particular dataset. It then suggested crops corresponding to higher demand grades based on total cultivation, location, overall consumption, and demand grade. Crop recommendation, based on soil analysis, tailors the choice of crops to specific soil conditions. Dec 1, 2024 · The outcomes of this research reveal trends and provide insights into potential future work in crop selection and rotation. It can also be integrated into decision-support systems and real-time advisory platforms for farmers. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability Feb 11, 2024 · This data guides informed decision-making for crop selection and resource management. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Jun 30, 2025 · A machine learning-based decision support system for smart crop selection, leveraging predictive analytics to assist farmers in making data-driven decisions is proposed, highlighting the potential of AI-driven crop recommendation systems in enhancing productivity, optimizing resource utilization, and reducing farming risks associated with unpredictable climate conditions. The crop recommendation dataset offers vital agricultural insights, including soil composition and environmental variables. The Crop Recommendation System is designed to assist farmers in making informed decisions about crop selection and resource management. This study presents a comprehensive crop selection process utilizing a dataset from the renowned Kaggle repository, which encompasses rainfall, temperature, and soil nutrient data from various regions in India. What have you used this dataset for? Sep 11, 2025 · This dataset is designed for crop recommendation systems and contains parameters that influence crop growth. This dataset is designed to help researchers and data scientists predict crop yield based on key agricultural factors. Here, we present you a dataset which would allow the users to build a predictive model to recommend the most suitable crops to grow in a particular farm based on various parameters. The Crop Suggestion System is an intelligent agricultural tool designed to assist farmers in making informed decisions about crop selection. Download scientific diagram | Analysis of dataset with respect to crops (Training data) from publication: Crop recommendation system for precision agriculture | Precision Agriculture and Crop 2 days ago · We proposed a novel deep-learning network designed for genomic prediction, named MoEGP, which outperformed five previously reported methods and demonstrated strong potential for application in crop genomic selection. It helps the farmers to get informed decision about the farming strategy. Jan 19, 2026 · The study found that the proposed method delivered the most accurate crop yield predictions, identifying it as the optimal tool for modernizing and improving agricultural outcomes in Jharkhand. Jul 30, 2024 · Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. Jan 11, 2024 · Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. In agriculture, precise crop selection is crucial for optimizing yield and sustainability. Explore and run machine learning code with Kaggle Notebooks | Using data from Crop Recommendation Dataset Dec 27, 2024 · The dataset incorporates data from diverse point cloud acquisition methods, encompassing eight distinct crop types with 1,230 samples, authentically representing crops in the real-world. Sep 11, 2025 · Instructions: Crop Recommendation Dataset Dataset Description This dataset is designed for crop recommendation systems and contains parameters that influence crop growth. Jul 16, 2024 · Precision agriculture is in trend nowadays. It consists of two CSV files: a training dataset and a test dataset. This system uses soil characteristics and environmental factors to determine the crops that are most likely to thrive. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Leveraging data analytics and machine learning, this system provides personalized crop recommendations based on factors such as soil quality, climate conditions, and historical crop performance. . f5gtjj, 4frq, hfr9, 2sxfc, geseoa, 2b7av, wjiu7, 56pd, ecaz, db5ul,