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FERTILIZER PREDICTION

Machine learning in python

ABSTRACT

Fertilizers play an important role in the field of agriculture. The common difficulty present among the Indian farmers are that they don’t opt for the proper soil based on their crop necessities. Because of this, productivity is affected. This problem of the farmers has been solved through precision agriculture. There are several fertilizers with varied ratio of NPK depending on the optimum quantity of nutrients to make a fertile soil which in turn produces a good yield. Each type of soil can have different kinds of features and different kinds of crops can grow on different types of soils. We need to know the optimum conditions for various crops to analyze which fertilizer suits the best for the type of crop growing in that particular soil. Machine Learning comes helpful in this case. We test and train the dataset to predict the accuracy and precision of the parameters given in the dataset

Project Components

  1. Data collection
  2. Data visualization
  3. Data cleaning
  4. Machine Learning- Random forest Classifier

Data Collection

The dataset containing the soil specific attributes which are collected from a website “Kaggle”, collected by researching various websites and sources.

The dataset can be referred in the attachement.

The attributes considered where temperature, humidity, moisture, NPK values, soil type, crop type and fertilizer name. The above stated parameters of soil play a major role in the crop's ability to absorb nutrients and fertilizers from the soil. For a good yield, the soil must provide acceptable environment for it. Soil is the anchor of the roots. The moisture holding capacity determines the crop ability to absorb nutrients primarily nitrogen, potassium, phosphorus. Texture, i.e. the soil type determines how porous the soil is and the comfort of air and water movement which is essential to prevent the plants from becoming waterlogged. The level of acidity or alkalinity (Ph) is a master variable which affects the availability of soil nutrients. The activity of microorganisms present in the soil and also the level of exchangeable aluminum can be affected by PH.

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