Employing Deep Convolutional Neural Networks for Enhanced Precision in Potato and Maize Leaf Disease Detection and Classification

Abstract

Advances in image processing and techniques in artificial intelligence have made it possible for computers to see and learn. This article introduced a technology that has utilised MobilenetV2 Deep Convolution Neural Network architecture to automatically identify and diagnose plant diseases from images. The identification and classification of plant diseases are now carried out by only human experts-crop extension agents, and farmers, expensive labour that is prone to mistakes. This study relies on dataset gathering as a technique of classifying and identifying plant diseases. It is a multistep process involving pre-process data on the raw set, mask green area of the leaf, remove green section, convert to grayscale and then obtain some characteristics, select, and classify with regard to disease management, etc. Two different types of plants, maize and potato, have been taken in consideration to show effectiveness of the outcome of the proposed model. The confusion matrix and classification performance report were used to evaluate the system. The dataset for potato and maize comprised 6228 and 6878 images, respectively, of leaves. Precise, recall, and F1-scores of 95.15%, 94.76%, and 94.93% were recorded as a cumulative performance across the datasets of potato and maize respectively. This translates to its resistance in picking most diseases for these crops, making it a resource that can be used with confidence in agriculture disease detection. The MobileNetV2 model performs well in both crops, especially for potato early blight and maize common rust. Lower performance in recognizing healthy potato leaves suggests that the feature space of healthy and diseased leaves may overlap. The MobileNetV2 model performed a robust ability in general in the detection of most diseases affecting both potato and maize leaves, but some specific areas need to be targeted for further enhancement.

Keywords

• Convolutional Neural Networks
• Feature Extraction
• Image Processing
• Maize Leaf
• MobileNetV2
• Potato Leaf

References

1. Najmi, A., Javed, S. A., Al Bratty, M., & Alhazmi, H. A. (2022). Modern Approaches in the Discovery and Development of Plant-Based Natural Products and Their Analogues as Potential Therapeutic Agents. Molecules, 27(2), 349. https://doi.org/10.3390/molecules27020349
2. Burda, I., Martin, A. C., Roeder, A. H. K., & Collins, M. A. (2023). The dynamics and biophysics of shape formation: Common themes in plant and animal morphogenesis. Developmental Cell, 58(24), 2850–2866. https://doi.org/10.1016/j.devcel.2023.11.003
3. Gao, L., & Cui, X. (2023). Climate change and food security: Plant science roles. Molecular Plant, 16(10), 1481–1483. https://doi.org/10.1016/j.molp.2023.09.019
4. Ray, M., Ray, A., Dash, S., Mishra, A., Achary, K. G., Nayak, S., & Singh, S. (2017). Fungal disease detection in plants: Traditional assays, novel diagnostic techniques and biosensors. Biosensors and Bioelectronics, 87, 708–723. https://doi.org/10.1016/j.bios.2016.09.032
5. Zechmann, B., & Zellnig, G. (2015). Microwave Assisted Rapid Diagnosis of Plant Virus Diseases by TEM. Microscopy and Microanalysis, 21(S3), 75–76. https://doi.org/10.1017/S1431927615001178
6. Markos, D., Mammo, G., & Worku, W. (2022). Principal component and cluster analyses based characterization of maize fields in southern central Rift Valley of Ethiopia. Open Agriculture, 7(1), 504–519. https://doi.org/10.1515/opag-2022-0105
7. Meghraoui, K., Sebari, I., Bensiali, S., & Ait El Kadi, K. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118–126. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/329
8. İncekara, Çetin Önder . (2023). Industrial internet of things (IIoT) in energy sector. Advanced Engineering Science, 3, 21–30. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/839

9. Kayıran, H. F. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1(2), 99–107. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/328
10. Kocalar, A. C. (2023). Sinkholes caused by agricultural excess water using and administrative traces of the process. Advanced Engineering Science, 3, 15–20. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/756
11. Ghayoomi , H. ., & Partohaghighi , M. . (2023). Investigating lake drought prevention using a DRL-based method. Engineering Applications, 2(1), 49–59. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/829
12. Pajaziti, A., Basholli , F. ., & Zhaveli , Y. . (2023). Identification and classification of fruits through robotic system by using artificial intelligence. Engineering Applications, 2(2), 154–163. Retrieved from
13. Hira, S., & Lande, S. (2022). Detection of fruit ripeness using image processing. International Journal of Health Sciences, 3874–3886. https://doi.org/10.53730/ijhs.v6nS6.10146
14. Hyder, U., & Talpur, M. R. H. (2024). Detection of cotton leaf disease with machine learning model. Turkish Journal of Engineering, 8(2), 380-393. https://doi.org/10.31127/tuje.1406755
15. Sofuoğlu, C. İ., & Bırant, D. (2024). Potato Plant Leaf Disease Detection Using Deep Learning Method. Journal of Agricultural Sciences, 30(1), 153-165. https://doi.org/10.15832/ankutbd.1276722
16. Irmak, G., & Saygılı, A. (2024). A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks. Journal of Agricultural Sciences, 30(2), 367-385. https://doi.org/10.15832/ankutbd.1332675
17. Karadeni̇Z, A. T., Başaran, E., & Çeli̇K, Y. (2023). Automatıc Classıfıcatıon Of Walnut Leaf Images Wıth Gradcam And Deep Learnıng. Current Trends in Computing Volume 1 Issue 2, 139-148. https://dergipark.org.tr/tr/pub/ctc/issue/83086/1430950
18. Polater, S. N., & Sevli, O. (2024). Deep Learning Based Classification for Alzheimer's Disease Detection Using MRI Images. Turkish Journal of Engineering, 8 (4), 729-740.
19. Gülgün, O. D., & Hamza, E. R. O. L. (2020). Classification performance comparisons of deep learning models in pneumonia diagnosis using chest x-ray images. Turkish Journal of Engineering, 4(3), 129-141. https://doi.org/10.31127/tuje.652358
20. Ramedani, Z., Omid, M., Keyhani, A., Shamshirband, S., & Khoshnevisan, B. (2014). Potential of radial basis function based support vector regression for global solar radiation prediction. Renewable and Sustainable Energy Reviews, 39, 1005–1011. https://doi.org/10.1016/j.rser.2014.07.108
21. Zinonos, Z., Gkelios, S., Khalifeh, A. F., Hadjimitsis, D. G., Boutalis, Y. S., & Chatzichristofis, S. A. (2022). Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology. IEEE Access, 10, 122–133. https://doi.org/10.1109/ACCESS.2021.3138050
22. Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., & Plümer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99. https://doi.org/10.1016/j.compag.2010.06.009
23. Ataman, F., & Eroğlu, H. (2024). Comparative Investigation of Deep Convolutional Networks in Detection of Plant Diseases. Türk Doğa Ve Fen Dergisi, 13(3), 37-49. https://doi.org/10.46810/tdfd.1477476
24. Anim-Ayeko, A. O., Schillaci, C., & Lipani, A. (2023). Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning. Smart Agricultural Technology, 4, 100178. https://doi.org/10.1016/j.atech.2023.100178
25. Gómez, F., Bravo, C., Ringler, I., Santander, C., González, F., Viscarra, F., Mardones, C., Contreras, B., Cornejo, P., & Ruiz, A. (2023). Evaluation of the Antifungal Potential of Grape Cane and Flesh-Coloured Potato Extracts against Rhizoctonia sp. in Solanum tuberosum Crops. Plants, 12(16), 2974. https://doi.org/10.3390/plants12162974
26. Tadesse Demissie, Y. (2019). Integrated Potato (Solanum Tuberosum L.) Late Blight (Phytophthora Infestans) Disease Management in Ethiopia. American Journal of BioScience, 7(6), 123. https://doi.org/10.11648/j.ajbio.20190706.16
27. Bista, S., & Adhikari, A. (2023). A Comprehensıve Strategy For Late Blıght Management In Potato And Tomato. Reviews In Food And Agriculture, 4(2), 50–53. https://doi.org/10.26480/rfna.02.2023.50.53
28. Omondi, D. O., Dida, M. M., Berger, D. K., Beyene, Y., Nsibo, D. L., Juma, C., Mahabaleswara, S. L., & Gowda, M. (2023). Combination of linkage and association mapping with genomic prediction to infer QTL regions associated with gray leaf spot and northern corn leaf blight resistance in tropical maize. Frontiers in Genetics, 14. https://doi.org/10.3389/fgene.2023.1282673
29. Lapajne, J., Vončina, A., Knapič, M., & Žibrat, U. (2023). 55. Potato plant disease classification by using deep learning and sparse sensing. Precision Agriculture ’23, 443–449. https://doi.org/10.3920/978-90-8686-947-3_55
30. Abebe, D. (2023). Min Review of Evaluation of Resistance Reaction of Maize varieties to Exserohilum turcicum(Pass) Leonard and Suggs Causing agent of Northern Corn Leaf Blight. Journal of Food and Nutrition, 2(2). https://doi.org/10.58489/2836-2276/019
31. Durai, S., Sujithra, T., & Iqbal, M. M. (2023). Image Classification for Potato Plant Leaf Disease Detection using Deep Learning. 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), 154–158. https://doi.org/10.1109/ICSCSS57650.2023.10169446
32. Li, X., Zhou, Y., Liu, J., Wang, L., Zhang, J., & Fan, X. (2022). The Detection Method of Potato Foliage Diseases in Complex Background Based on Instance Segmentation and Semantic Segmentation. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.899754
33. Sahu, S. L., & Chintala Bhargavi, R. (2023). Prediction of Diseases in Potato Plant using Pre-trained and Traditional Machine Learning Models. 2023 4th International Conference for Emerging Technology (INCET), 1–8. https://doi.org/10.1109/INCET57972.2023.10170149
34. Satheesh Amal, Barman Dhritiraj, & Sarmah Sonia. (2023). Green Tech: An Android Application for the Automatic Identification of Potato Leaf Diseases using Deep Learning. ADBU-Journal of Engineering Technology, 12(2)
35. Feng, J., Hou, B., Yu, C., Yang, H., Wang, C., Shi, X., & Hu, Y. (2023). Research and Validation of Potato Late Blight Detection Method Based on Deep Learning. Agronomy, 13(6), 1659. https://doi.org/10.3390/agronomy13061659
36. Yang, S., Xing, Z., Wang, H., Gao, X., Dong, X., Yao, Y., Zhang, R., Zhang, X., Li, S., Zhao, Y., & Liu, Z. (2023). Classification and localization of maize leaf spot disease based on weakly supervised learning. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1128399
37. Esmael Ahmed, & Kedir Abdu. (2023). Maize Disease Detection using Color Cooccurrence Features. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 01–10. https://doi.org/10.32628/CSEIT2390140
38. Acharya, A. K., Rout, M., Padhy, S., Jena, S., & Sahu, P. K. (2023). Screening based sparse classification method for the detection of corn leaf diseases. Journal of Statistics & Management Systems, 26(1), 147–158. https://doi.org/10.47974/JSMS-954
39. Olawuyi, O., & Viriri, S. (2023). Plant Diseases Detection and Classification Using Deep Transfer Learning. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 459, 270–288. https://doi.org/10.1007/978-3-031-25271-6_17
40. Kundu, N., Rani, G., Dhaka, V. S., Gupta, K., Nayaka, S. C., Vocaturo, E., & Zumpano, E. (2022). Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning. Artificial Intelligence in Agriculture, 6, 276–291. https://doi.org/10.1016/j.aiia.2022.11.002
41. Çakmak, M. (2024b). Automatic Maize Leaf Disease Recognition Using Deep Learning. Sakarya University Journal of Computer and Information Sciences, 7(1), 61–76. https://doi.org/10.35377/saucis. . .1418505
42. Rashid, J., Khan, I., Ali, G., Almotiri, S. H., AlGhamdi, M. A., & Masood, K. (2021). Multi-Level Deep Learning Model for Potato Leaf Disease Recognition. Electronics, 10(17), 2064. https://doi.org/10.3390/electronics10172064 https://publish.mersin.edu.tr/index.php/enap/article/view/974
43. Jasim, M. K. (2019). Image Noise Removal Techniques : A Comparative Analysis. International Journal of Science and Applied Information Technology, 8(6), 24–29. https://doi.org/10.30534/ijsait/2019/01862019
44. Sahoo Santanu Kumar, & Subudhi Asit Kumar. (2019). Using Color and Texture Feature Extraction Technique to Retrieve Image. International Journal of Innovative Technology and Exploring Engineering, 8(11S), 915–917. https://doi.org/10.35940/ijitee.K1166.09811S19
45. Yang, Y., Zhang, L., Du, M., Bo, J., Liu, H., Ren, L., Li, X., & Deen, M. J. (2021). A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions. Computers in Biology and Medicine, 139, 104887. https://doi.org/10.1016/j.compbiomed.2021.104887
46. Gulzar, Y. (2023). Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique. Sustainability, 15(3), 1906. https://doi.org/10.3390/su15031906