A smart agriculture solution for crop disease detection : A deep learning
*Aditya SharmaCorresponding authoraditya11dec@gmail.comDepartment of Computer Science EngineeringI.K. Gujral Punjab Technical UniversityKapurthala, Punjab, 144603, India0009-0002-0259-8387View full profile → , Raman Kumarer.ramankumar@aol.inDepartment of Computer Science EngineeringI.K. Gujral Punjab Technical UniversityKapurthala, Punjab, 144603, India0000-0003-4661-4764View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2025
- Published Online:
- 03 Jul 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2108
- Pages:
- 2583–2599
Abstract
Crop diseases can greatly affect crop yields, and early detection is thus important to reduce losses. Recently, different automatic plant disease recognition systems have been developed. This paper introduces four deep learning models—VGG16, EfficientNetB0, ResNet-50, and GoogleNet to recognize four rice diseases based on plant leaf images. The models were trained and tested using different public datasets, including the Rice Leaf Disease Image dataset in Mendeley. The Dense Layer model of VGG16 has an accuracy rate of 99.747%, while that of VGG16 2D-CNN is 99.916%. The Dense Layer model also recorded a validation loss of 1.3880 with a test accuracy of 27.004%, while the 2D-CNN EfficientNetB0 model has a validation loss of 1.3830 with the same test accuracy. ResNet-50 with Dense Layer decreased the validation loss to 0.8121 and had a test accuracy of 70.211%, whereas ResNet-50 2D-CNN achieved a validation loss of 0.7238 with a test accuracy of 69.789%. Lastly, the GoogleNet model with Dense Layer provided 99.916% accuracy, and the GoogleNet 2D-CNN model had 99.831% accuracy. The outcomes show that such sophisticated deep learning models diagnose rice diseases accurately, providing intelligent agriculture solutions for disease detection at the early stage. Farmers can be assisted by deploying AI-based systems to ensure optimal crop yields and quality. Overall, this research proves that plant disease diagnosis can be done highly effectively using AI-based approaches and presents a green solution to an important agricultural issue.
Keywords
Subject Classifications
References
[1] H. T. Rauf, B. A. Saleem, M. I. U. Lali, M. A. Khan, M. Sharif, and S. A. C. Bukhari, “A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning,” Data Brief, vol. 26, Art. no. 104340 (2019), doi: 10.1016/j.dib.2019.104340.
[2] R. Mahum, H. Munir, Z.-U.-N. Mughal, M. Awais, F. Khan, M. Saqlain, S. Mahamad, and I. Tlili, “A novel framework for potato leaf disease detection using an efficient deep learning model,” Human Ecol. Risk Assess., vol. 29, pp. 1–24 (2022), doi: 10.1080/10807039.2022.2064814.
[3] V. A. Gupta, M. V. Padmavati, R. C. Saxena, P. K. Patnaik, and R. K. Tamrakar, “A study on image processing techniques and deep learning techniques for insect identification,” Karbala Int. J. Mod. Sci., vol. 9, no. 2 (May 2023), doi: 10.33640/2405-609x.3289.
[4] R. Patel and B. M. Joshi, “A survey on the plant leaf disease detection techniques,” Int. J. Adv. Res. Comput. Commun. Eng., pp. 229–231 (Mar. 2017) doi: 10.17148/ijarcce.2017.6143.
[5] A. Ahmad, D. Saraswat, and A. E. Gamal, “A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools,” Smart Agric. Technol., vol. 3, Art. no. 100083 (Feb. 2023), doi: 10.1016/j.atech.2022.100083.
[6] S. V. Militante and B. D. Gerardo, “Detecting sugarcane diseases through adaptive deep learning models of convolutional neural network,” in Proc. IEEE 6th Int. Conf. Engineering Technologies and Applied Sciences (ICETAS), Kuala Lumpur, Malaysia, pp. 1–5 (2019), doi: 10.1109/ICETAS48360.2019.9117332.
[7] S. B. Jagtap and S. M. Hambarde, “Agricultural plant leaf disease detection and diagnosis using image processing based on morphological feature extraction,” IOSR J. VLSI Signal Process., vol. 4, no. 5, pp. 24–30 (Jan. 2014), doi: 10.9790/4200-04512430.
[8] K. R. Gavhale and U. Gawande, “An overview of the research on plant leaves disease detection using image processing techniques,” Int. J. Sci. Res. Publ., vol. 4, no. 3, pp. 1–4 (2014).
[9] S. Amritraj, N. Hans, and C. P. Diana Cyril, “An automated and fine-tuned image detection and classification system for plant leaf diseases,” in Proc. Int. Conf. Recent Adv. Electr., Electron., Ubiquitous Commun., Comput. Intell. (RAEEUCCI), Chennai, India, pp. 1–5 (2023), doi: 10.1109/RAEEUCCI57140.2023.10134461.
[10] C. Ozden, “Apple leaf disease detection and classification based on transfer learning,” Turkish J. Agric. For., vol. 45, pp. 775–783 (2021), doi: 10.3906/tar-2010-100.
[11] M. H. Saleem, S. Khanchi, J. Potgieter, and K. M. Arif, “Image-based plant disease identification by deep learning meta-architectures,” Plants, vol. 9, no. 11, Art. no. 1451 (2020), doi: 10.3390/plants9111451.
[12] S. Talasila, K. Rawal, G. Sethi, M. Sanjay, and M. S. P. Reddy, “Black gram plant leaf disease (BPLD) dataset for recognition and classification of diseases using computer-vision algorithms,” Data Brief, vol. 45, Art. no. 108725 (Dec. 2022), doi: 10.1016/j.dib.2022.108725.
[13] A. Haridasan, J. Thomas, and E. D. Raj, “Deep learning system for paddy plant disease detection and classification,” Environ. Monit. Assess., vol. 195, no. 1 (2022), doi: 10.1007/s10661-022-10656-x.
[14] M. Janani and R. Jebakumar, “Detection and classification of groundnut leaf nutrient level extraction in RGB images,” Adv. Eng. Softw., vol. 175, Art. no. 103320 (Jan. 2023), doi: 10.1016/j.advengsoft.2022.103320.
[15] K. Bashir, M. Rehman, and M. Bari, “Detection and classification of rice diseases: An automated approach using textural features,” Mehran Univ. Res. J. Eng. Technol., vol. 38, no. 1, pp. 239–250 (Jan. 2019), doi: 10.22581/muet1982.1901.20.
[16] V. Singh and A. K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques,” Inf. Process. Agric., vol. 4, no. 1, pp. 41–49 (Mar. 2017), doi: 10.1016/j.inpa.2016.10.005.
[17] C.-L. Chung, K.-J. Huang, S.-Y. Chen, M.-H. Lai, Y.-C. Chen, and Y.-F. Kuo, “Detecting Bakanae disease in rice seedlings by machine vision,” Computers and Electronics in Agriculture, vol. 121, pp. 404–411 (2016), doi: 10.1016/j.compag.2016.01.008.
[18] H. Yang, X. Deng, H. Shen, Q. Lei, S. Zhang, and N. Liu, “Disease detection and identification of rice leaf based on improved detection transformer,” Agriculture, vol. 13, no. 7, p. 1361 (Jul. 2023), doi: 10.3390/agriculture13071361.
[19] K. C. Kamal, Z. Yin, M. Wu, and Z. Wu, “Depthwise separable convolution architectures for plant disease classification,” Computers and Electronics in Agriculture, vol. 165, p. 104948 (2019), doi: 10.1016/j.compag.2019.104948.
[20] J. Chen, D. Zhang, and Y. A. Nanehkaran, “Identifying plant diseases using deep transfer learning and enhanced lightweight network,” Multimedia Tools and Applications, vol. 79, pp. 31497–31515 (2020), doi: 10.1007/s11042-020-09669-w.
[21] K. Adem, M. M. Ozguven, and Z. Altas, “A sugar beet leaf disease classification method based on image processing and deep learning,” Multimedia Tools and Applications, vol. 82, pp. 12577–12594 (2023), doi: 10.1007/s11042-022-13925-6.
[22] S. U. Rahman, F. Alam, N. Ahmad, and S. Arshad, “Image processing based system for the detection, identification and treatment of tomato leaf diseases,” Multimedia Tools and Applications, vol. 82, no. 6, pp. 9431–9445 (2022), doi: 10.1007/s11042-022-13715-0.
[23] V. Pooja, R. Das, and V. Kanchana, “Identification of plant leaf diseases using image processing techniques,” in Proc. IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, India, pp. 130–133 (2017), doi: 10.1109/TIAR.2017.8273700.
[24] A. M. Fayyaz, K. A. Al-Dhlan, S. U. Rehman, M. Raza, W. Mehmood, M. Shafiq, and J.-G. Choi, “Leaf Blights Detection and Classification in Large Scale Applications,” Intell. Autom. Soft Comput., vol. 31, no. 1, pp. 507–522 (2022), doi: 10.32604/iasc.2022.016392. :contentReference[oaicite:0]{index=0}
[25] J. Barbedo, “Plant disease identification from individual lesions and spots using deep learning,” Biosystems Engineering, vol. 180, pp. 96–107 (2019), doi: 10.1016/j.biosystemseng.2019.02.002.
[26] Y. M. A. Algani, O. J. Márquez, L. M. R. Bravo, C. Kaur, M. S. A. Ansari, and B. K. Bala, “Leaf disease identification and classification using optimized deep learning,” Measurement: Sensors, vol. 25, p. 100643 (2023), doi: 10.1016/j.measen.2022.100643.
[27] L. Jia, T. Wang, Y. Chen, Y. Zang, X. Li, H. Shi, and L. Gao, “MobileNet-CA-YOLO: An Improved YOLOv7 Based on the MobileNetV3 and Attention Mechanism for Rice Pests and Diseases Detection,” IEEE-style Agriculture, vol. 13, no. 7, Art. no. 1285 (2023), doi: 10.3390/agriculture13071285. :contentReference[oaicite:0]{index=0}
[28] M. Ahmad, S. H. Hamid, S. Yousaf, S. T. Shah, and M. O. Ahmad, “Optimizing pretrained convolutional neural networks for tomato leaf disease detection,” Complexity, vol. 2020, pp. 1–6 (2020), doi: 10.1155/2020/8812019.
[29] A. S. Zamani, L. Anand, K. P. Rane, P. Prabhu, A. M. Buttar, H. Pallathadka, A. Raghuvanshi, and B. N. Dugbakie, “[Retracted] Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection,” J. Food Qual., vol. 2022, Art. no. 1598796, 7 pp. (2022), doi: 10.1155/2022/1598796.
[30] A. K. M. S. Mehedi, S. Hosain, S. Ahmed, S. T. Promita, R. K. Muna, M. Hasan, and M. T. Reza, “Plant Leaf Disease Detection Using Transfer Learning and Explainable AI,” in Proc. IEEE 13th Annu. Inf. Technol., Electron. Mobile Commun. Conf. (IEMCON) (2022), doi: 10.1109/IEMCON56893.2022.9946513.
