Reseach Article
Detecting Droplets for Crop Spraying Systems using Machine Learning
by
Debmalya Ray
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 4 |
| Year of Publication: 2025 |
| Authors: Debmalya Ray |
10.5120/jaai202420
|
Debmalya Ray . Detecting Droplets for Crop Spraying Systems using Machine Learning. Journal of Advanced Artificial Intelligence. 1, 4 ( Jan 2025), 26-30. DOI=10.5120/jaai202420
@article{
10.5120/jaai202420,
author = {
Debmalya Ray
},
title = { Detecting Droplets for Crop Spraying Systems using Machine Learning },
journal = {
Journal of Advanced Artificial Intelligence
},
issue_date = { Jan 2025 },
volume = { 1 },
number = { 4 },
month = { Jan },
year = { 2025 },
pages = {
26-30
},
numpages = {9},
url = {
https://jaaionline.phdfocus.com/archives/volume1/number4/detecting-droplets-for-crop-spraying-systems-using-machine-learning/
},
doi = { 10.5120/jaai202420 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-02-01T00:09:13.383094+05:30
%A Debmalya Ray
%T Detecting Droplets for Crop Spraying Systems using Machine Learning
%J Journal of Advanced Artificial Intelligence
%V 1
%N 4
%P 26-30
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Agricultural Development combined with technology has made great progress in recent years, making it possible to improve the yield for farmers. This project combines deep learning algorithms with spraying technology to design a machine vision precision real-time targeting spraying system for field scenarios. Highly efficient mechanized nozzles are used to spray and apply nutrients and pesticides to crops so that farmers can increase production and mitigate the gap between supplies and demands. We employ high-speed visualization [8] to quantify the impact and evaporation of a droplet on a solid surface. This will also help us to identify the density/area covered by a single spray at a time and correct the delta part left to be covered at first work. This paper is focused on using image classification techniques with a computer vision algorithm to extract the parameters required from a single image at a time and convert it into structured data so that an unsupervised algorithm can cluster the regions based on density.
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Index Terms
Computer Science
Information Sciences
Keywords