Comparative Investigation of Deep Convolutional Networks in Detection of Plant Diseases

Year 2024, , 37 - 49, 26.09.2024

Fikriye Ataman , Halil Eroğlu

https://doi.org/10.46810/tdfd.1477476

Abstract

Abstract: Preserving plant health and early detection of diseases are crucial in modern agriculture. Artificial intelligence techniques, particularly deep learning networks, are employed for this purpose. In this study, disease recognition was conducted using leaf images from various plant species. The study encompassed important agricultural products such as apples, strawberries, grapes, corn, peppers, and potatoes among the plant species considered. Among the deep learning networks, popular architectures like AlexNet, Vgg16, MobileNetV2, and Inception were compared. The Inception V3 model achieved the highest success rate of 92%, followed by the AlexNet architecture with a success rate of 91%. Among these networks, the InceptionV3 model yielded the best results. The InceptionV3 model effectively learned from plant leaf images and accurately distinguished between diseased and healthy leaves. These findings demonstrate that AI-based systems can be efficiently utilized for disease recognition and prevention in the agriculture sector. In this study, the performance of the InceptionV3 model in disease recognition on plant leaves was analyzed in detail, emphasizing the role of deep learning networks in agricultural applications.

Keywords

Deep learning, Image processing, Convolutional neural networks, Plant disease detection

References

• Hinton G, LeCun Y, Bengio Y. Deep learning. Nature. 2015;521(7553):436–44.
• Benfenati A, Causin P, Oberti R, Stefanello G. Unsupervised deep learning techniques for automatic detection of plant diseases: reducing the need of manual labelling of plant images. Journal of Mathematics in Industry. 2023 Dec 1;13(1).
• Ahmed I, Yadav PK. A systematic analysis of machine learning and deep learning based approaches for identifying and diagnosing plant diseases. Sustainable Operations and Computers. 2023 Jan 1;4:96–104.
• Shovon MSH, Mozumder SJ, Pal OK, Mridha MF, Asai N, Shin J. PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection. IEEE Access. 2023;11:34846–59.
• Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images. Cluster Computing. 2023 Apr 1;26(2):1297–317.
• Ahmad A, Gamal A El, Saraswat D. Toward Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions. IEEE Access. 2023;11:9042–57.
• Moupojou E, Tagne A, Retraint F, Tadonkemwa A, Wilfried D, Tapamo H, et al. FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning. IEEE Access. 2023;11:35398–410.
• Guan H, Fu C, Zhang G, Li K, Wang P, Zhu Z. A lightweight model for efficient identification of plant diseases and pests based on deep learning. Frontiers in Plant Science. 2023;14.
• Shoaib M, Shah B, EI-Sappagh S, Ali A, Ullah A, Alenezi F, et al. An advanced deep learning models-based plant disease detection: A review of recent research. Vol. 14, Frontiers in Plant Science. Frontiers Media SA; 2023.
• Pandian JA, Kumar VD, Geman O, Hnatiuc M, Arif M, Kanchanadevi K. Plant Disease Detection Using Deep Convolutional Neural Network. Applied Sciences (Switzerland). 2022 Jul 1;12(14).
• Alzahrani MS, Alsaade FW. Transform and Deep Learning Algorithms for the Early Detection and Recognition of Tomato Leaf Disease. Agronomy. 2023 May 1;13(5).
• Khalid MM, Karan O. Deep Learning for Plant Disease Detection. International Journal of Mathematics, Statistics, and Computer Science. 2023 Nov 18;2:75–84.
• Bouacida I, Farou B, Djakhdjakha L, Seridi H, Kurulay M. Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves. Information Processing in Agriculture. 2024;
• Yang W, Yuan Y, Zhang D, Zheng L, Nie F. An Effective Image Classification Method for Plant Diseases with Improved Channel Attention Mechanism aECAnet Based on Deep Learning. Symmetry [Internet]. 2024 Apr 8;16(4):451. Available from: https://www.mdpi.com/2073-8994/16/4/451
• Joseph DS, Pawar PM, Chakradeo K. Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning. IEEE Access. 2024;12:16310–33.
• Saraswat S, Singh P, Kumar M, Agarwal J. Advanced detection of fungi-bacterial diseases in plants using modified deep neural network and DSURF. Multimedia Tools and Applications. 2024 Feb 1;83(6):16711–33.
• Kumar A, Kumar P, Suman K. Deep Learning for Automated Diagnosis of Plant Diseases: A Technological Approach. Vol. 20, J. Electrical Systems. 2024.
• Chin PW, Ng KW, Palanichamy N. Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study. Journal of Informatics and Web Engineering. 2024 Feb 14;3(1):155–68.
• Kolluri J, Dash SK, Das R. International Journal of Intellıgent Systems and Applıcatıons In Engıneerıng Plant Disease Identification Based on Multimodal Learning [Internet]. Vol. 2024, Original Research Paper International Journal of Intelligent Systems and Applications in Engineering IJISAE. 2024. Available from: www.ijisae.org
• Korra S, Bhaskar T, Ramana N, Bhukya S, Rajender N. International Journal of Intellıgent Systems And Applıcatıons In Engıneerıng An Efficient Guided Backpropagation Approach for Detection of Plant Diseases Using Deep Learning Models [Internet]. Vol. 2024, Original Research Paper International Journal of Intelligent Systems and Applications in Engineering IJISAE. 2024. Available from: www.ijisae.org
• Bhagat S, Kokare M, Haswani V, Hambarde P, Taori T, Ghante PH, et al. Advancing real-time plant disease detection: A lightweight deep learning approach and novel dataset for pigeon pea crop. Smart Agricultural Technology. 2024 Mar 1;7.
• Aliff M, Luqman M, Yusof MI, Sani NS, Syafiq MU, Sadikan SFN, et al. Utilizing Aerial Imagery and Deep Learning Techniques for Identifying Banana Plants Diseases. ITM Web of Conferences. 2024;60:00013.
• Sofuoglu CI, Birant D. Potato Plant Leaf Disease Detection Using Deep Learning Method. Tarim Bilimleri Dergisi. 2024 Sep 1;30(1):153–65.
• Najim MH, Abdulateef SK, Alasadi AH. Early detection of tomato leaf diseases based on deep learning techniques. IAES International Journal of Artificial Intelligence. 2024 Mar 1;13(1):509–15.
• Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture. 2019 Jun 1;161:272–9.
• Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture. 2018 Feb 1;145:311–8.
• Chohan* M, Khan A, Chohan R, Katpar SH, Mahar MS. Plant Disease Detection using Deep Learning. International Journal of Recent Technology and Engineering (IJRTE) [Internet]. 2020 May 30;9(1):909–14. Available from: https://www.ijrte.org/portfolio-item/A2139059120/
• Liu J, Wang X. Plant diseases and pests detection based on deep learning: a review. Vol. 17, Plant Methods. BioMed Central Ltd; 2021.
• Jakjoud F, Hatim A, Bouaaddi A. Deep learning application for plant diseases detection. In: ACM International Conference Proceeding Series. Association for Computing Machinery; 2019.
• Wan H, Lu Z, Qi W, Chen Y. Plant disease classification using deep learning methods. In: ACM International Conference Proceeding Series. Association for Computing Machinery; 2020. p. 5–9.
• Barbedo JGA. Factors influencing the use of deep learning for plant disease recognition. Biosystems Engineering. 2018 Aug 1;172:84–91.
• Akshai KP, Anitha J. Plant disease classification using deep learning. In: 2021 3rd International Conference on Signal Processing and Communication, ICPSC 2021. Institute of Electrical and Electronics Engineers Inc.; 2021. p. 407–11.
• Bhattarai S. New plant diseases dataset. 2024.
• Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks [Internet]. 2012 [cited 2023 Oct 22]. Available from: https://papers.nips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
• Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. 2014 Sep 4; Available from: http://arxiv.org/abs/1409.1556
• Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. MobileNetV2: Inverted Residuals and Linear Bottlenecks. 2018 Jan 12; Available from: http://arxiv.org/abs/1801.04381
• Szegedy C, Vanhoucke V, Ioffe S, Shlens J. Rethinking the Inception Architecture for Computer Vision [Internet]. 2015 [cited 2023 Oct 28]. Available from: https://arxiv.org/pdf/1512.00567.pdf