Farming Futures: Leveraging Machine Language for Potato Leaf Disease Forecasting and Yield Optimization

Title

Farming Futures: Leveraging Machine Language for Potato Leaf Disease Forecasting and Yield Optimization

Creator

Vijayalakshmi A.; Nidin A.; Das D.

Description

Crop yield prediction is of paramount importance in modern agriculture. It serves as a linchpin for ensuring food security, efficient resource management, risk mitigation, environmental sustainability, and socioeconomic development. Accurate predictions enable us to maintain a stable food supply, optimize resource allocation, and manage the uncertainties associated with climate and market fluctuations. By fostering sustainable farming practices, crop yield prediction also plays a crucial role in reducing environmental impact and promoting rural development. Integrating artificial intelligence (AI) and machine learning (ML) in modern agricultural practices offers the potential to revolutionize the way we produce food, making it more sustainable, efficient, and resilient. This study has demonstrated the effectiveness of convolutional neural networks (CNNs) in the classification of potato leaf disease, achieving remarkable results with a test loss of 0.0757 and a test accuracy of 0.9741. 2024 Taylor & Francis Group, LLC.

Source

Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture, pp. 200-215.

Date

2024-01-01

Publisher

CRC Press

Coverage

Vijayalakshmi A., CHRIST (Deemed to be University), Bangalore, India; Nidin A., CHRIST (Deemed to be University), Bangalore, India; Das D., CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-104003723-2; 978-103261892-0

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1201/9781003485179-11

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195961109&doi=10.1201%2f9781003485179-11&partnerID=40&md5=d3515dd5a321d62414f2326910ce27ca