Design a recommendation system for accurate crop selection based on the various NPK, temperature, humidity, rainfall,pH. To improve crop productivity by providing predictions of high accuracy and efficiency through the machine learning algorithm.

The Crop Recommendation System targets to offer records to the farmers at the soil and crop traits. With this statistics the farmer can recognize which crop can be cultivated

paper introduces an accessible and user-friendly solution for crop recommendations and yield predictions. Users provide inputs such as temperature, humidity, soil pH, and rainfall. To enhance accuracy, a hybrid approach using K-nearest neighbor (KNN) and Random Forest (RF) algorithms is employed. The K-nearest

Means, In this paper we are going implement crop recommendation system using random forest algorithm. The model allowed to train upon large dataset and the performance of the recommendation system is measured using accuracy score.

recommendation system through an ensemble model with majority voting techniques using Random Forest and K Nearest Neighbor as learner to recommend suitable crop based on soil parameters with high specific accuracy and efficiency.

Web based crop recommendation system application using Machine Learning - Shubha-ml/Web-based-crop-recommendation-system-application-using-Machine-Learning ... Use Case Diagram. Activity Diagram. Sequence Diagram. ... Web based crop recommendation system application using Machine Learning Topics. machine-learning random-forest classification ...

This simple crop recommender system was trained using Random Forest Algorithm in giving recommendations to farmers the best and suitable crop based on an Indian Crop Recommendation Dataset.

May 13, 2024 · The use of the Random Forest algorithm for predicting optimum crops based on soil parameters unveils a versatile approach applicable to a multitude of domains.

This Crop Recommendation System uses machine learning and the Random Forest Algorithm to guide farmers in selecting the best crops based on an Indian Crop Recommendation Dataset. By inputting key soil parameters (N, P, K, pH), alongside weather data (temperature, humidity, rainfall), the system delivers tailored, region-specific crop suggestions.

With the use of an intelligent system called Crop Recommender, this project attempts to help Indian farmers choose the best crop to grow based on the qualities of the soil, as well as external parameters like temperature and rainfall. Indian economy is significantly influenced by the agricultural sector.