ScholarLens Back to search
2022•Unpublished venueRequires access

Predicting Crop Yield Using Decision Tree Regressor

S.P. Mani Raj, Siddhesh Patle, Subash Rajendran

Open publisher page 4 citations

Abstract

Farmers are the backbone of our country. A large part of our economy relies on the agricultural sector. From Generations of agricultural knowledge farmers very well know which crop will be suitable for their land, but they may not be able to predict the yield of their crops and reason behind that is rapid change of weather conditions. Methodology used here focuses on predicting the crop yield based on state, district, season, rainfall and temperature. Based on these parameters, a decision tree regressor algorithm is trained and a model is created. A UI is provided on which farmers can input some of these parameters which are then given to the flask backend along with the current rainfall and temperature data given by an API based on current location. The farmers are provided with the predicted yield returned from the backend after running on the model.

About this research paper

What this paper is about

Farmers are the backbone of our country. A large part of our economy relies on the agricultural sector. From Generations of agricultural knowledge farmers very well know which crop will be suitable for their land, but they may not be able to predict the yield of their crops and reason behind that is rapid change of weather conditions. Methodology used here focuses on predicting the crop yield based on state, district, season, rainfall and temperature. Based on these parameters, a decision tree regressor algorithm is trained and a model is created. A UI is provided on which farmers can input some of these parameters which are then given to the flask backend along with the current rainfall and temperature data given by an API based on current location. The farmers are provided with the predicted yield returned from the backend after running on the model.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Farmers are the backbone of our country. A large part of our economy relies on the agricultural sector. From Generations of agricultural knowledge farmers very well know which crop will be suitable for their land, but they may not be able to predict the yield of their crops and reason behind that is rapid change of weather conditions. Methodology used here focuses on predicting the crop yield based on state, district, season, rainfall and temperature. Based on these parameters, a decision tree regressor algorithm is trained and a model is created. A UI is provided on which farmers can input some of these parameters which are then given to the flask backend along with the current rainfall and temperature data given by an API based on current location. The farmers are provided with the predicted yield returned from the backend after running on the model.

Key concepts: Yield (engineering), Agriculture, Decision tree, Agricultural engineering, Crop, Tree (set theory), Crop yield, Current (fluid)

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Predicting Crop Yield Using Decision Tree Regressor — Research Paper | ScholarLens