Crop recommendation is one of the most important aspects of precision agriculture. Crop recommendations are based on a number of factors. Precision agriculture seeks to define these criteria on a site-by-site basis in order to address crop selection issues. While the "site-specific" methodology has improved performance, there is still a need to monitor the systems' outcomes.Precision agriculture systems aren't all created equal. However, in agriculture, it is critical that the recommendations made are correct and precise, as errors can result in significant material and capital loss.

This dataset was build by augmenting datasets of rainfall, climate and fertilizer data available for India.

• N - Ratio of Nitrogen content in soil
• P - Ratio of Phosphorous content in soil
• K - Ratio of Potassium content in soil
• Temperature - temperature in degree Celsius
• Humidity - relative humidity in %
• ph - ph value of the soil
• Rainfall - rainfall in mm

• Data Analysis
  • All columns contain outliers except for N.
• Performance Evaluation
  • Splitting the dataset by 80 % for training set and 20 % validation set.
• Training and Validation
  • GausianNB gets a higher accuracy score than other classification models.
  • GaussianNB ( 99 % accuracy score )
• Performance Results
  • Training Score: 99.5%
  • Validation Score: 99.3%

Live Demo: https://ai-crop-recommender.herokuapp

user interface of the app. *https://drive.google.com/file/d/1FAEXBcgXCQkklOBdEyNS5tH2q6fle6bg/view?usp=sharing

#conclusion ->Now-a-days farmers facing lots of problems and they don‟t know the proper information regarding crops to grow and to cultivate. This proposed system helps farmers to know the right crop to grow. ->Proposed system predicts the crops using Machine Learning techniques based on the soil tested results. ->This system also useful to agriculture departments to predicts the right crop in right time. If we have such kind of automation, will be useful to farmers and agricultural field. ->The goals that have been achieved by the developed system are, simplified and reduced the manual work, Large volumes of data can be stored and It provides Smooth workflow.

• https://www.prolim.com/crop-recommendation-system-using-machine-learning-for-digital-farming/
• https://www.irjet.net/archives/V4/i12/IRJET-V4I12179.pdf
• http://sersc.org/journals/index.php/IJAST/article/view/30399
• https://www.researchgate.net/publication/345247916_Crop_Plantation_Recommendation_using_Feature_Extraction_and_Machine_Learning_Techniques
• https://ieeexplore.ieee.org/document/8768790
• https://towardsdatascience.com/farmeasy-crop-recommendation-portal-for-farmers-48a8809b421c
• http://agri.ckcest.cn/ass/8185d605-6c4d-4d8a-b280-c867c2304d42.pdf
• https://www.kaggle.com/atharvaingle/crop-recommendation-dataset