Smart Agriculture System for Pomegranate Disease Detection and Yield Prediction

Abstract— This project is about using deep learning to find and identify different diseases in pomegranate fruits. The main goal is to help farmers catch diseases early and protect their crops to get better harvests. We used a dataset of around 2,000 pictures of pomegranates. These images were divided into five groups: Healthy, Alternaria, Anthracnose, Bacterial Blight, and Cercospora. Before training, we prepared the images by resizing them, normalizing the colors, and adding some changes to make the model learn better (this is called data augmentation). We trained two models: one was a simple CNN (Convolutional Neural Network) that we made ourselves, and the other was a pre-trained DenseNet121 model that we fine-tuned. Both models were trained for 20 rounds (called epochs). The DenseNet121 model did better, getting 94.2% accuracy on the validation set, while the custom CNN got 87.5%. We also made a web application using Flask, where users can upload images of pomegranates and get real-time results. The app tells whether the fruit is healthy or has a disease, gives tips on how to deal with the disease, and shows an estimate of how much crop might be lost.

Deep Learning, Convolutional Neural Networks (CNN), DenseNet121, Image Classification, Precision Agriculture

1. Narayanan, K. L.; Krishnan, R. S.; Robinson, Y. H.; Julie, E. G.; Vimal, S.; Saravanan, V.; Kaliappan, M. Banana Plant Disease Classification Using Hybrid Convolutional Neural Network. Comput. Intell. Neurosci 2022, 2022, 1–13. DOI: 10.1155/2022/9153699. [Google Scholar] [Crossref]

2. L.M. Abouelmagd, M.Y. Shams, H.S. Marie, A.E. Hassanien An optimized capsule neural networks for tomato leaf disease classification Eurasip J. Image Video Process., 2024 (1) (2024), p. 2 [Google Scholar] [Crossref]

3. N. Ahmad, H.M. Shahzad Asif, G. Saleem, M. Younus Usman, S. Anwar, M. Anjum Riszwan Leaf image-based plant disease identification using colour and texture features Wirel. Pers. Commun., 121 (2) (2021), pp. 1139-1168, 10.1007/s11277-021-09054-2 [Google Scholar] [Crossref]

4. S. Ahmed, M.S.B. Alam, M. Hassan, M.R. Rozbu, T. Ishtiak, N. Rafa, et al. Deep learning modelling techniques: current progress, applications, advantages, and challenges Artif. Intell. Rev., 56 (11) (2023), pp. 13521-13617 [Google Scholar] [Crossref]

5. L. Alzubaidi, J. Zhang, A.J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al- Shamma, et.al. Review of deep learning: concepts, CNNarchitectures, challenges, applications, future directions J. Big Data, 8 (2021), pp. 1 -74 [Google Scholar] [Crossref]

6. S. Angel, E. Mary, M. Dikshna, B. Athisaya, A.D. Andrushia, M. Neebha Deep learning based disease detection in tomatoes In 2021 3rd International Conference on Signal Processing and Communication (ICPSC), IEEE (2021), pp. [Google Scholar] [Crossref]

7. -392, 10.1109/ICSPC51351.2021.9451731 [Google Scholar] [Crossref]

8. Chakrabarty, S.T. Ahmed, M.F.U. Islam, S.M. Aziz, S.S. Maidin An interpretable fusion model integrating lightweight CNN and transformer architectures for rice leaf disease identification Eco. Inform., 82 (2024), Article 102718 [Google Scholar] [Crossref]

9. Sharath, D. M.; Kumar, S. A.; Rohan, M. G., Suresh, K.V.; Prathap, C. Disease Detection in Plants Using Convolutional Neural Network. Third International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE: India, 2020; pp 389–394. [Google Scholar] [Crossref]

10. A.Diana, Andrushia, T. Mary Neebha, A. Trephena Patricia, S. Umadevi, N. Anand, A. Varshney Image-based disease classification in grape leaves using convolutional capsule network Soft. Comput., 27 (3) (2023), pp. 1457-1470. [Google Scholar] [Crossref]

11. Z. Guo, Y. Ni, H. Gao, G. Ding, Y. Zeng A dual-branch selective attention capsule network for classifying kiwifruit soft rot with hyper spectral images Sci. Rep., 14 (1) (2024), p. 10664 [Google Scholar] [Crossref]

• Advanced Techniques for Fake News Detection on Twitter Using NLP and AI: A Comprehensive Review
• Review of Self Compacting Geopolymer Concrete Using Slag Sand as Fine Aggregate
• Performance of Local Construction Contractors – Case Study of Registered Contractors in Monrovia, Liberia
• Cross-Cultural Perspectives on Innovation Management in Multinational Organizations
• Modeling of Reaction Between Dissolved Oxygen (DO) And Biological Oxygen Demand (BOD) in Degradation River