this research investigates the use of artificial neural networks (ANNs) and image processing techniques for monitoring black eggplant crops, including for classification, disease detection, and potential yield estimation. A dataset of eggplant images was analysed, image pre-processing was performed, features were extracted via convolutional neural networks (CNNs), and classification/regression models were built. The results show that CNN-based methods achieve high accuracy in disease classification and crop classification tasks. The implications for precision agriculture and reduced environmental impact are discussed. The aim of this study is to use artificial intelligence, specifically networks, to examine diseases affecting eggplant, given its importance as a crop. Practitioners should begin with transfer learning using pre-trained CNNs for disease detection, progressively integrating multispectral sensors and recurrent networks for temporal modeling. The development of a dedicated black eggplant monitoring case report would significantly advance precision horticulture for this economically vital crop . The study could potentially be extended to other crops.
| Published in | International Journal of Applied Agricultural Sciences (Volume 12, Issue 2) |
| DOI | 10.11648/j.ijaas.20261202.14 |
| Page(s) | 48-53 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Eggplant, Artificial Neural Network, Convolutional Neural Network, Image Processing, Crop Monitoring, Disease Detection
| [1] | Krishnaswamy Rangarajan, A. & Purushothaman, C. Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM. Sci. Rep. 10, (2020). |
| [2] | Saad, I. H., et al. An automated approach for eggplant disease recognition using transfer learning. Bulletin of Electrical Engineering and Informatics, Vol. 11, No. 5, Oct 2022: 2789-2798. |
| [3] | Liu, J., et al. EggplantDet: An efficient lightweight model for eggplant disease detection. (2025). ScienceDirect. |
| [4] | Sun, L., et al. Research on Classification Method of Eggplant Seeds based on multispectral imaging and machine learning. 2021. |
| [5] | Balasubramanian, V.; Guo, W.; Chandra, A.; Desai, S. V. Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey. arXiv 2020, arXiv: 2006.11391 |
| [6] | Van Dijk, A. D. J.; Kootstra, G.; Kruijer, W.; de Ridder, D. Machine learning in plant science and plant breeding. iScience 2021, 24, 101890. |
| [7] | Montesinos López, O. A.; Montesinos López, A.; Crossa, J. Random Forest for Genomic Prediction. In Multivariate Statistical Machine Learning Methods for Genomic Prediction; Springer: Cham, Switzerland, 2020. |
| [8] | Gutiérrez, S.; Tardaguila, J.; Fernández-Novales, J.; Diago, M. P. Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer. PLoS ONE 2015, 10, e0143197. |
| [9] | Yan, J.; Xu, Y.; Cheng, Q.; Jiang, S.; Wang, Q.; Xiao, Y.; Ma, C.; Yan, J.; Wang, X. LightGBM: Accelerated genomically designed crop breeding through ensemble learning. Genome. Biol. 2021, 22, 271. |
| [10] | Karahan, T.; Nabiyev, V. Plant identification with convolutional neural networks and transfer learning. Pamukkale Univ. J. Eng. Sci. 2021, 27, 638–645. |
| [11] | Liang, W., Zhang, H., Zhang, G. F. & Cao, H. X. Rice blast disease recognition using a deep convolutional neural network. Sci. Rep. 9, 2869, |
| [12] | Brahimi, M., Boukhalfa, K. & Moussaoui Deep learning for tomato disease: classification and symptoms visualization. Appl. Artif. Intell. 31, 299–315 (2017). |
| [13] | Kamilaris, A. & Prenafeta-Boldú, F. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90, |
APA Style
Aljuboury, M. M. (2026). Application of Artificial Neural Networks and Image-Based Analysis for Black Eggplant (Solanum Melongena) Crop Monitoring: Case Report. International Journal of Applied Agricultural Sciences, 12(2), 48-53. https://doi.org/10.11648/j.ijaas.20261202.14
ACS Style
Aljuboury, M. M. Application of Artificial Neural Networks and Image-Based Analysis for Black Eggplant (Solanum Melongena) Crop Monitoring: Case Report. Int. J. Appl. Agric. Sci. 2026, 12(2), 48-53. doi: 10.11648/j.ijaas.20261202.14
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijaas.20261202.14,
author = {Mina M. Aljuboury},
title = {Application of Artificial Neural Networks and Image-Based Analysis for Black Eggplant (Solanum Melongena) Crop Monitoring: Case Report},
journal = {International Journal of Applied Agricultural Sciences},
volume = {12},
number = {2},
pages = {48-53},
doi = {10.11648/j.ijaas.20261202.14},
url = {https://doi.org/10.11648/j.ijaas.20261202.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijaas.20261202.14},
abstract = {this research investigates the use of artificial neural networks (ANNs) and image processing techniques for monitoring black eggplant crops, including for classification, disease detection, and potential yield estimation. A dataset of eggplant images was analysed, image pre-processing was performed, features were extracted via convolutional neural networks (CNNs), and classification/regression models were built. The results show that CNN-based methods achieve high accuracy in disease classification and crop classification tasks. The implications for precision agriculture and reduced environmental impact are discussed. The aim of this study is to use artificial intelligence, specifically networks, to examine diseases affecting eggplant, given its importance as a crop. Practitioners should begin with transfer learning using pre-trained CNNs for disease detection, progressively integrating multispectral sensors and recurrent networks for temporal modeling. The development of a dedicated black eggplant monitoring case report would significantly advance precision horticulture for this economically vital crop . The study could potentially be extended to other crops.},
year = {2026}
}
TY - JOUR T1 - Application of Artificial Neural Networks and Image-Based Analysis for Black Eggplant (Solanum Melongena) Crop Monitoring: Case Report AU - Mina M. Aljuboury Y1 - 2026/04/23 PY - 2026 N1 - https://doi.org/10.11648/j.ijaas.20261202.14 DO - 10.11648/j.ijaas.20261202.14 T2 - International Journal of Applied Agricultural Sciences JF - International Journal of Applied Agricultural Sciences JO - International Journal of Applied Agricultural Sciences SP - 48 EP - 53 PB - Science Publishing Group SN - 2469-7885 UR - https://doi.org/10.11648/j.ijaas.20261202.14 AB - this research investigates the use of artificial neural networks (ANNs) and image processing techniques for monitoring black eggplant crops, including for classification, disease detection, and potential yield estimation. A dataset of eggplant images was analysed, image pre-processing was performed, features were extracted via convolutional neural networks (CNNs), and classification/regression models were built. The results show that CNN-based methods achieve high accuracy in disease classification and crop classification tasks. The implications for precision agriculture and reduced environmental impact are discussed. The aim of this study is to use artificial intelligence, specifically networks, to examine diseases affecting eggplant, given its importance as a crop. Practitioners should begin with transfer learning using pre-trained CNNs for disease detection, progressively integrating multispectral sensors and recurrent networks for temporal modeling. The development of a dedicated black eggplant monitoring case report would significantly advance precision horticulture for this economically vital crop . The study could potentially be extended to other crops. VL - 12 IS - 2 ER -
Al-Mussaib Technical Institute, Al-Furat Al-Awsat Technical University, Babylon, Iraq
Information