An Optimized Deep Learning Approach for Early Weed Detection in Chili Crop Habitats
Received: 9 April 2025 | Revised: 7 May 2025, 17 May 2025, 26 May 2025, and 9 June 2025 | Accepted: 14 June 2025 | Online: 20 August 2025
Corresponding author: Rajesh Yakkundimath
Abstract
Plant identification is a significant activity in agriculture, botany, and environmental protection for the precise classification of plants into species to ensure an efficient control of the crops, measurement of diversity, and ecosystems control. Conventional processes of plant classification based on manual identification by specialists are resource-intensive, inefficient, and error-prone. To address these limitations, this research explores Deep Learning (DL)-based approaches for automatic plant classification using chilli weeds as an example. This research evaluated and compared the performance of three Convolutional Neural Network (CNN) architectures—MobileNetV2, ResNet50, and VGG16—trained on a self-collected image dataset from the College of Horticultural Engineering and Food Technology (DSLD CHEFT), Devihosur, Haveri, comprising four plant species. Among the models, MobileNetV2 achieved the highest classification accuracy of 96.6%, outperforming ResNet50 with a 95.0% accuracy, and VGG16 with an 88.0% accuracy. MobileNetV2's lightweight design offers a distinct advantage, which enables efficient inference and makes the model highly suitable for future deployment on edge devices with limited computational resources. This study highlights the potential of CNN-based systems for practical applications in agriculture, such as automated weed detection and precision farming.
Keywords:
MobileNetV2, ResNet50, VGG16, classification, Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), chilli crop, weed detectionDownloads
References
A. J. Ishak, M. M. Mustafa, N. M. Tahir, and A. Hussain, "Weed detection system using support vector machine," in 2008 International Symposium on Information Theory and Its Applications, Auckland, New Zealand, Dec. 2008, pp. 1–4.
G. H. Krishnan and T. Rajasenbagam, “A Comprehensive Survey for Weed Classification and Detection in Agriculture Lands,” Journal of Information Technology and Digital World, vol. 3, no. 4, pp. 281–289, Apr. 2022.
Y. Sun, Y. Liu, G. Wang, and H. Zhang, “Deep Learning for Plant Identification in Natural Environment,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1–6, 2017.
N. C. Kundur and P. B. Mallikarjuna, “Deep Convolutional Neural Network Architecture for Plant Seedling Classification,” Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9464–9470, Dec. 2022.
N. Makanapura, C. Sujatha, P. R. Patil, and P. Desai, "Classification of plant seedlings using deep convolutional neural network architectures," Journal of Physics: Conference Series, vol. 2161, no. 1, Jan. 2022, Art.no 012006.
X. Guo, "Automatic detection of tomato leaf disease using an adopted deep learning algorithm," Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7909–7921, Apr. 2024.
A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, “Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations,” Frontiers in Plant Science, vol. 15, Mar. 2024.
L. G. Divyanth, D. S. Guru, P. Soni, R. Machavaram, M. Nadimi, and J. Paliwal, “Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications,” Algorithms, vol. 15, no. 11, Oct. 2022, Art. no. 401.
F. A. Wichmann and R. Geirhos, “Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?,” Annual Review of Vision Science, vol. 9, no. 1, pp. 501–524, Sep. 2023.
J. S. K. and A. S., “Transfer Learning Based Network Performance Comparison of the Pre-Trained Deep Neural Networks using MATLAB,” Open International Journal of Informatics, vol. 10, no. 1, pp. 27–40 May 2022.
O. Vermesan and D. Marples, Advancing Edge Artificial Intelligence: System Contexts, 1st ed. New York: River Publishers, 2024.
K. C. Kiran, H. N. Rithin, K. Sathwik, L. S. Shreya, and B. T. Venkatesh Murthy, “Smart Autonomous Agriculture Robot for Multipurpose Farming Application,” in 2024 Second International Conference on Networks, Multimedia and Information Technology, Bengaluru, India, Aug. 2024, pp. 1–7.
A. S. Paymode and V. B. Malode, “Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG,” Artificial Intelligence in Agriculture, vol. 6, pp. 23–33, 2022.
N. Islam et al., “Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm,” Agriculture, vol. 11, no. 5, Apr. 2021, Art. no. 387.
“Empirical Study of Multi-class Weed Classification Using Deep Learning Network Through Transfer Learning,” in Lecture Notes in Networks and Systems, Singapore: Springer Nature Singapore, 2023, pp. 1–12.
G. Konnurmath and S. Chickerur, “GPU Shader Analysis and Power Optimization Model,” Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12925–12930, Feb. 2024.
Downloads
How to Cite
License
Copyright (c) 2025 Sandeep Telkar, Rajesh Yakkundimath, Likewin Thomas, Yasmeen Shaikh
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
