Deep Learning-Based Early Detection Optimization for Rice Leaf Diseases to Support Sustainable Local Agriculture
-
Putrama Alkhairi
*
STIKOM Tunas Bangsa, Pematangsiantar,
Indonesia
- Agus Perdana Windarto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
- Mesran Mesran Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Indonesia
- Roznim Roznim Universiti Pendidikan Sultan Idris, Tanjong Malim, Malaysia (*) Corresponding Author
Abstract
Rice leaf diseases such as Bacterial Blight and Blast are major threats to rice productivity that directly impact food security and the sustainability of local agriculture. This study aims to develop and optimize a deep learning-based early detection system for rice leaf diseases using a Convolutional Neural Network (CNN) architecture, specifically the Inception_v3 model. The research method includes five main stages, namely collecting rice leaf image datasets, data pre-processing (resize, normalization, and augmentation), CNN model design, model training and evaluation, and performance optimization through the application of different optimizer algorithms. Two model variants were tested and compared, namely Inception_v3 Basics with the RMSprop optimizer and Inception_v3 Optimization with the Adam optimizer. Experimental results showed that the Inception_v3 Optimization model provided the best performance, with a Precision value of 0.9672, Recall of 0.8939, F1-score of 0.9291, Balanced Accuracy of 0.9297, Matthews Correlation Coefficient (MCC) of 0.8578, Cohen's Kappa of 0.8573, and AUC ROC of 0.98. These results indicate that the Adam optimizer is able to accelerate convergence and improve model accuracy compared to RMSprop, while producing a more stable and efficient classification system. Thus, this study successfully demonstrated that the optimized Inception_v3 architecture can be used effectively for early detection of rice leaf diseases and has high potential for integration into smart farming systems to support sustainable, technology-based local agricultural practices.
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References
M. Iqbal, “Effects of fungicide application timings and genetic resistance of rice varieties on the reduction of percent severity of narrow brown leaf spot disease,” Pakistan J. Agric. Sci., vol. 60, no. 3, pp. 337–343, 2023, doi: 10.21162/PAKJAS/23.209.
J. Zheng, “High-efficiency real-time rice leaf disease classification using convolutional neural network accelerator on FPGA for edge computing in precision agriculture,” Meas. J. Int. Meas. Confed., vol. 256, 2025, doi: 10.1016/j.measurement.2025.118122.
S. Sakhamuri, “Optimal Training Ensemble of Classifiers for Classification of Rice Leaf Disease,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 3, pp. 94–105, 2023, doi: 10.14569/IJACSA.2023.0140311.
P. T. Pavithra, “A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification,” Int. J. Comput. Methods Exp. Meas., vol. 12, no. 1, pp. 35–43, 2024, doi: 10.18280/ijcmem.120104.
B. D. Thanh, “Rice Leaf Diseases Detection Using Deep Learning Ensemble Model,” Gmsarn Int. J., vol. 18, no. 3, pp. 308–314, 2024.
Y. Bo, “Identification and detection of rice leaf diseases by YOLOv5 neural network based on improved SPP-x,” J. Chinese Agric. Mech., vol. 44, no. 9, pp. 190–197, 2023, doi: 10.13733/j.jcam.issn.2095-5553.2023.09.027.
E. Norouzi, “Expression of OsWRKY45 and OsAOS2 genes in the signaling pathways of salicylic acid and jasmonic acid in leaf and spike blast disease of rice,” Agric. Biotechnol. J., vol. 15, no. 4, pp. 105–124, 2023, doi: 10.22103/jab.2023.21017.1457.
M. S. H. Shovon, S. J. Mozumder, O. K. Pal, M. F. Mridha, N. Asai, and J. Shin, “PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection,” IEEE Access, vol. 11, no. March, pp. 34846–34859, 2023, doi: 10.1109/ACCESS.2023.3264835.
Z. Liu, “Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models,” IEEE Open J. Eng. Med. Biol., vol. 3, pp. 189–201, 2022, doi: 10.1109/OJEMB.2022.3219725.
N. Abirami, “P2P-COVID-GAN: Classification and segmentation of COVID-19 lung infections from CT images using GAN,” Int. J. Data Warehous. Min., vol. 17, no. 4, pp. 101–118, 2021, doi: 10.4018/IJDWM.2021100105.
S. Lara, “The Human Monocyte—A Circulating Sensor of Infection and a Potent and Rapid Inducer of Inflammation,” Int. J. Mol. Sci., vol. 23, no. 7, 2022, doi: 10.3390/ijms23073890.
Y. S. Taspinar, “Classification by a stacking model using CNN features for COVID-19 infection diagnosis,” J. X Ray Sci. Technol., vol. 30, no. 1, pp. 73–88, 2022, doi: 10.3233/XST-211031.
D. K. Verma, “Classifying COVID-19 and viral pneumonia lung infections through deep convolutional neural network model using chest X-Ray images,” J. Med. Phys., vol. 47, no. 1, pp. 57–64, 2022, doi: 10.4103/jmp.jmp_100_21.
B. Akhila, K. Parichitha, M. Thrisha, B. Deepthi, and G. Manjula, “Deep Learning-Based Detection of Rice Leaf Diseases for Smart Agriculture,” J. Sci. Eng. Technol. Manag. Sci., vol. 2, no. 4, pp. 111–122, 2025, doi: 10.63590/jsetms.2025.v02.i04.pp111-122.
C. Dewi, F. Y. Bilaut, H. J. Christanto, and G. Dai, “Deep Learning for the Classification of Rice Leaf Diseases Using YOLOv8,” Math. Model. Eng. Probl., vol. 11, no. 11, pp. 3025–3034, 2024, doi: 10.18280/mmep.111115.
J. Li, “Coordinated automatic generation control of interconnected power system with imitation guided exploration multi-agent deep reinforcement learning,” Int. J. Electr. Power Energy Syst., vol. 136, 2022, doi: 10.1016/j.ijepes.2021.107471.
Y. Ji, “Deep mining of mobile learning data based on multi-scale clustering analysis,” Int. J. Contin. Eng. Educ. Life-Long Learn., vol. 33, no. 4, pp. 456–467, 2023, doi: 10.1504/IJCEELL.2023.132414.
S. Shivadekar, “Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 1, pp. 241–250, 2023.
K. Balaraman, “Hybrid ResNet and Bidirectional LSTM based Deep Learning Model for Cardiovascular Disease Detection using PPG Signals,” J. Mach. Comput., vol. 3, no. 3, pp. 351–359, 2023, doi: 10.53759/7669/jmc202303030.
X. Cui, “A contrast-enhanced-CT-based classification tree model for classifying malignancy of solid lung tumors in a Chinese clinical population,” J. Thorac. Dis., vol. 13, no. 7, pp. 4407–4417, 2021, doi: 10.21037/jtd-21-588.
I. Kayadibi, “An Early Retinal Disease Diagnosis System Using Oct Images Via Cnn-Based Stacking Ensemble Learning,” Int. J. Multiscale Comput. Eng., vol. 21, no. 1, pp. 1–25, 2023, doi: 10.1615/IntJMultCompEng.2022043544.
G. Li, “Optimizing deep neural networks on intelligent edge accelerators via flexible-rate filter pruning,” J. Syst. Archit., vol. 124, 2022, doi: 10.1016/j.sysarc.2022.102431.
O. S. Warang, “Optimizing phalsa (cv. Local) growth, flowering, and yield parameters through round-the-year pruning and fertilizer management,” J. Appl. Hortic., vol. 25, no. 2, pp. 117–119, 2023, doi: 10.37855/jah.2023.v25i02.20.
A. Harshavardhan, S. Cheerla, A. Parkavi, S. A. Latha Mary, K. Qureshi, and H. R. Mhaske, “Deep learning modified neural networks with chicken swarm optimization-based lungs disease detection and severity classification,” J. Electron. Imaging, vol. 32, no. 06, May 2023, doi: 10.1117/1.JEI.32.6.062603.
L. Pham, “CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 8, pp. 2938–2947, 2021, doi: 10.1109/JBHI.2021.3064237.
K. Muthusamy, “A Novel Feature Extraction and Siamese Zeiler and Fergus Forward Taylor Network-Based Rice Plant Leaf Disease Detection,” J. Phytopathol., vol. 173, no. 3, 2025, doi: 10.1111/jph.70074.
A. Kumar, “An XNOR-ResNet and spatial pyramid pooling-based YOLO v3-tiny algorithm for Monkeypox and similar skin disease detection,” Imaging Sci. J., vol. 71, no. 1, pp. 50–65, 2023, doi: 10.1080/13682199.2023.2175423.
P. Preethi, R. Swathika, S. Kaliraj, R. Premkumar, and J. Yogapriya, “Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification,” Food Energy Secur., vol. 13, no. 5, pp. 1–10, 2024, doi: 10.1002/fes3.70001.
K. A. Mohamed, “COVID-19 Disease Detection based on X-Ray Image Classification using CNN with GEV Activation Function,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 9, pp. 890–898, 2022, doi: 10.14569/IJACSA.2022.01309103.
A. P. Windarto, T. Herawan, and P. Alkhairi, “Early Detection of Breast Cancer Based on Patient Symptom Data Using Naive Bayes Algorithm on Genomic Data,” in Artificial Intelligence, Data Science and Applications, Y. Farhaoui, A. Hussain, T. Saba, H. Taherdoost, and A. Verma, Eds., Cham: Springer Nature Switzerland, 2024, pp. 478–484. doi: 10.1007/978-3-031-48465-0_64.
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