Journal of Computers, Mechanical and Management

Rights and permissions

© The Author(s) 2025.

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits the non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative Commons License and changes need to be indicated if there are any. The images or other third-party material in this article are included in the article's Creative Commons License, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons License and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this License, visit: httpshttps://creativecommons.org/licenses/by-nc/4.0/

S. S. Salve, S. S. Chakraborty, S. Gandhewar, S. S. Girhe, A deep learning framework for smart agriculture: real time weed classification using convolutional neural network, Journal of Smart Sensors and Computing, 2025, 1(1), 25205, doi: https://doi.org/10.64189/ssc.25205.

Abstract

Agricultural sector being the foundation of food supply and raw material production contributes significantly to the GDP growth and value chain. Thus, the effective elimination of weed in modern day agriculture is essential as the current world scenario demands for efficient and resourceful ways for crop cultivation and harvesting. The urgent need for elimination of weeds arises due to their tendency to extricate all the essential minerals and moisture that the crops require for their appropriate growth. The main objective of this study is to successfully acquire live video feed as input, classification into categories of crop, weed and none. Finally, upon detection of weed the spraying mechanism releases a predetermined amount of herbicide upon the weed. A total of 5471 image samples were captured to train the CNN model. The prototype mentioned in this paper uses Convolutional Neural Network (CNN) technique for feature extraction, Fully Connected Layers or Dense Layers (FCLs) for classification using SoftMax as the activation function respectively. The activation function also here is being used to remove all negative (less significant) values. Also, a comprehensive comparison was made between the CNN and YOLOv4 technique and performance parameters of both were evaluated. The CNN technique achieved an accuracy of 95.50% whereas YOLOv4 achieved 91.00%. Finally, the F1 Score was evaluated to be 96.25% and 91.96% respectively. Compared to existing models, our prototype demonstrated higher accuracy and real-time adaptability in field conditions, proving suitable for autonomous weed management systems. Unlike earlier systems that depended mostly on stored images or fixed datasets, our approach stands out by using a live video feed to identify weeds in real-time. It’s built on a mobile platform that can automatically spray herbicides, making precision farming possible without the need for constant human supervision.

Graphical Abstract

Novelty statement

A robust Weed detection and elimination system is the needed in-order to efficiently boostup the agriculture sector.