Predicting Agriculture Using Machine Learning

ABSTRACT for Predicting Agriculture Using Machine Learning:

Predicting Agriculture Using Machine Learning : The Agriculture plays a dominant role in the growth of country’s economy. Climate and other environmental changes has become a major threat in the agriculture field. Agriculture is the main occupation of India. More than 70% of the population is involved in agriculture and its ancillary.

In order to feed the expanding population there is a need to incorporate the latest technologies and tools in the agriculture sector. With the help of machine learning algorithms the crop productivity can be increased by many folds. Big data provide facilities like data storage, data processing, and data analysis with accuracy, hence its use in the field of agriculture can benefit farmers and nation’s economic growth.

In this work, a precision agriculture model is presented to suggest farmers, which crop to cultivate according to field conditions. Focusing mainly on the agriculture in Indian region, the model uses a Naïve Bayes classifier to recommend about the crop to the farmers. It also suggests which crop can be grown in a specific given environment. The prediction analysis is most useful type of data which is performed today.

The prediction analysis can be done by gathering historical information to generate future trends. So, the knowledge of what has happened previously is used to provide the best valuation of what will happen in future with predictive analysis. Crop production analysis is one of the applications of prediction analysis.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED for Predicting Agriculture Using Machine Learning:

Naive Bayes Algorithm.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating system : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework :
Frontend : HTML, CSS, JavaScript.

Python Projects

Predicting Agriculture Using Machine Learning