Smart Agriculture: Improving Crop Health

• Author(s): Prashant Chawla ; Dr. K. C. Tripathi ; Dr. M. L. Sharma
• Paper ID: 1704740
• Page: 1177-1180
• Published Date: 30-06-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 12 June-2023

Abstract

Pest damage to plants and crops has an impact on the nation's agricultural output. In most cases, farmers or professionals watch the plants carefully for signs of illness. However, this procedure is frequently time-consuming, costly, and unreliable. Results from automatic detection employing image processing methods are quick and precise. This study uses deep convolutional networks to establish a new method for developing illness detection models that is backed by leaf image categorization. The area of precision agriculture has a possibility to grow and improve the practice of precise plant protection as well as the market for computer vision applications. A quick and simple system is made possible by the approach utilized and a wholly original manner of training.

Keywords

Plant Disease Detection, Machine Learning, Image Processing, Deep Learning, Convolutional Neural Network

Citations

IRE Journals:
Prashant Chawla , Dr. K. C. Tripathi , Dr. M. L. Sharma "Smart Agriculture: Improving Crop Health" Iconic Research And Engineering Journals Volume 6 Issue 12 2023 Page 1177-1180

IEEE:
Prashant Chawla , Dr. K. C. Tripathi , Dr. M. L. Sharma "Smart Agriculture: Improving Crop Health" Iconic Research And Engineering Journals, 6(12)