Agricultural Crop Recommendations based on Productivity and Season

ABSTRACT:

As a coastal state, Tamil Nadu faces uncertainty in agriculture which decreases its production. With more population and area, more productivity should be achieved but it cannot be reached. Farmers have words-of-mouth in past decades but now it cannot be used due to climatic factors. Agricultural factors and parameters make the data to get insights about the Agri-facts. Growth of IT world drives some highlights in Agriculture Sciences to help farmers with good agricultural information. Intelligence of applying modern technological methods in the field of agriculture is desirable in this current scenario. Machine Learning Techniques develops a well-defined model with the data and helps us to attain predictions. Agricultural issues like crop prediction, rotation, water requirement, fertilizer requirement and protection can be solved. Due to the variable climatic factors of the environment, there is a necessity to have a efficient technique to facilitate the crop cultivation and to lend a hand to the farmers in their production and management. This may help upcoming agriculturalists to have a better agriculture. A system of recommendations can be provided to a farmer to help them in crop cultivation with the help of data mining. To implement such an approach, crops are recommended based on its climatic factors and quantity. Data Analytics paves a way to evolve useful extraction from agricultural database. Crop Dataset has been analyzed and recommendation of crops is done based on productivity and season

ALGORITHM / MODEL USED:

Decision Tree Classifier

PROJECT OUTPUT VIDEO:

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.
• Coding Language : Python
• Web Framework : Flask

REFERENCE:

Vaishnavi.S, Shobana.M, .Sabitha.R, Karthik.S, “Agricultural Crop Recommendations based on Productivity and Season”, IEEE Conference, 2021.