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Machine Learning based Regression Model for Prediction of Soil Surface Humidity over Moderately Vegetated Fields

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Abstract:

Agriculture is one of the major revenue producing sectors of India and a source of survival. Numerous seasonal, economic and biological patterns influence the crop production but unpredictable changes in these patterns lead to a great loss to farmers. These risks can be reduced when suitable approaches are employed on data related to soil type, temperature, atmospheric pressure, humidity and crop type. Whereas, crop and weather forecasting can be predicted by deriving useful insights from these agricultural data that aids farmers to decide on the crop they would like to plant for the forthcoming year leading to maximum profit. This paper presents a survey on the various algorithms used for weather, crop yield, and crop cost prediction.

 

Existing System:

The agriculture sector needs a huge up gradation in order to survive the changing conditions of Indian economy. Along with the advances in machines and technologies used in farming, useful and accurate information about different matters also plays a significant role in it. This information is being gathered by the use of remote sensors, satellite images, surveys etc. This information along with the knowledge of subject experts and researchers should be readily available to the farmers in order to exploit its potential worth.

 

Disadvantage:

  1. As the amount of such information is increasing gradually.
  2. We will identify certain patterns in words slowly start understanding.
  3. It also provides an insight into the troubles faced by Indian farmers and how they can be resolved using these techniques.

 

Proposed System:

This scenario mainly concentrates on weather forecasting, crop yield prediction and crop cost forecasting. These factors help the farmers to cultivate the best food crops and raise the right animals with accordance to environmental components. Also, the farmers can adapt to climate changes to some degree by shifting planting dates, choosing varieties with different growth duration, or changing crop rotations. For experimental analysis, the statistical numeric data related to agriculture is undertaken. Whereas, the clustering based techniques and supervised algorithms are utilized for managing the collected statistical data. Additionally, the suitable classification methods like Support Vector Machine (SVM), neural networks are employed for better classification outcome.

 

Advantage:

  1. India’s agriculture consists of numerous crops, with the major crops of rice and wheat.
  2. Indian farmers growing pulses, sugarcane and also, non-food items like cotton, tea, coffee, and so on.
  3. These techniques will help in predicting the rainfall, crop yield forecasting and cost prediction of crops.

 

 

System Architecture:

 

Hardware and Software requirements:

Hardware:

  1. OS – Windows 10 (32 or 64 bit)
  2. RAM – 4GB

 

Software:

  1. Python IDLE
  2. Anaconda
  3. Jupyter Notebook
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  • Price
    Free
  • Instructor pantech team
  • Duration 15 Hrs
  • Enrolled 0 student
  • Access 3 Months

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