Using agricultural big data analytics in plant breeding and genetics to increase food yield

Document Type : Research Paper

Authors

1 Department of CS & IT, Kalinga University, Raipur, India.

2 Department of CS & IT, Kalinga University, Raipur, India.

10.22103/jab.2025.24002.1610

Abstract

Objective
When it comes to a healthy economy and population, the agriculture sector is essential. Smart Agriculture (SA) is a game-changing strategy that optimizes agricultural techniques with the use of cutting-edge technology like Big Data Analytics and the Internet of Things (IoT), in response to the rising need for food on a worldwide scale. The Internet of Things (IoT) gathers massive quantities of data from farms, allowing for more accurate disease control, irrigation methods, and crop output predictions. The goal of this research is to predict and improve grape plant production using an N-stage Convolutional Neural Network (CNN) trained using data from the SA database.

Materials and Methods
Optimal irrigation scheduling and amount prediction methods are also implemented in the research via the use of Machine Learning approaches. One useful method for early detection and treatment of plant illnesses is being investigated in this research: a Double Generative Adversarial Network (DGAN). This network might be used by farmers.

Results
The primary goal of this study is to develop a multi-stage convolutional neural network (CNN) model that can considerably boost agricultural output, with a focus on grape production.


Conclusions
A comprehensive strategy for grape plant development management is offered by the model via the integration of critical characteristics such as irrigation scheduling and disease diagnosis. Farmers are able to maximize their resources and output with the aid of this method, which also enhances the accuracy of yield predictions and facilitates better management decisions. In order to increase food production on a worldwide scale and promote sustainable agricultural techniques, this study's findings may lead to the wider use of Smart Agriculture methods.

Keywords

References

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Agricultural Biotechnology Journal
Volume 16, Issue 4
January 2025
Pages 335-348

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