Predictive Models in Agriculture: Combating Crop Diseases with Advanced Data Analytics
Keywords:
Convolutional neural network, Multiple Twin SVM, Support Vector MachinesAbstract
This research evaluates the performance of three advanced convolutional neural network (CNN) architectures—Xception, InceptionV3, and VGG19—on an image classification task using transfer learning with pre-trained ImageNet weights. Each model was fine-tuned for a specific dataset and assessed using key performance metrics such as accuracy, precision, recall, and F1-score. To enhance generalization, data augmentation techniques like rescaling, shifting, zooming, and flipping were applied during training, while validation and test data were rescaled for unbiased evaluation. The Xception model achieved an impressive overall accuracy of 95%, performing well across most classes, though it exhibited lower recall for class 7. The InceptionV3 model surpassed Xception, attaining an accuracy of 97%, but encountered difficulties in classifying instances from classes 7 and 8, where precision-recall trade-offs were evident. The VGG19 model, with a total accuracy of 94%, excelled in most classes but showed reduced precision and recall for classes 1 and 7.
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David M. Rizzo, Maureen Lichtveld, Jonna A. K. Mazet, Eri Togami & Sally A. Miller “Plant health and its effects on food safety and security in a One Health framework: four case studies” One Health Outlook volume 3, Article number: 6 (2021)
GEORGE N. AGRIOS, “ Plant Pathology “ (Fifth Edition), 2005.
P. A. Nazarov, D. N. Baleev, M. I. Ivanova, L. M. Sokolova, and M. V. Karakozova” Infectious Plant Diseases: Etiology, Current Status, Problems and Prospects in Plant Protection” Acta Naturae. 2020 Jul-Sep; 12(3): 46–59.
Aakanksha Rastogi, Ritika Arora, Shanu Sharma” Leaf disease detection and grading using computer vision technology & fuzzy logic” Conference: 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)-IEEE.
Andrew J.,Jennifer Eunice,Daniela Elena Popescu,M. Kalpana Chowdary and Jude Hemanth” Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications” Agronomy 2022, 12(10), 2395.
Ashok, G. Kishore, V. Rajesh, S. Suchitra, S.G. Sophia, B. Pavithra”Tomato leaf disease detection using deep learning techniques”2020 5th International Conference on Communication and Electronics Systems (ICCES), IEEE (2020), pp. 979-983.
H. Durmuş, E.O. Güneş, M. Kırcı” Disease detection on the leaves of the tomato plants by using deep learning”2017 6th International Conference on Agro-Geoinformatics, IEEE (2017), pp. 1-5.
R.G. De Luna, E.P. Dadios, A.A. Bandala” Automated image capturing system for deep learning based tomato plant leaf disease detection and recognition” TENCON 2018-2018 IEEE Region 10 Conference, IEEE (2018), pp. 1414-1419.
Mohit Agarwal , Abhishek Singh , Siddhartha Arjaria , Amit Sinha , Suneet Gupta ” ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network” Procedia Computer Science ,Volume 167, 2020, Pages 293-301.
Chittabarni Sarkar , Deepak Gupta , Umesh Gupta , Barenya Bikash Hazarika “Leaf disease detection using machine learning and deep learning: Review and challenges” Applied Soft Computing,Volume 145, September 2023, 110534.
Alexander A. Hernandez; Joferson L. Bombasi; Ace C. Lagman” Classification of Sugarcane Leaf Disease using Deep Learning Algorithms” 2022 IEEE 13th Control and System Graduate Research Colloquium (ICSGRC) : IEEE
Sammed Abhinandan Upadhye, Maneetkumar Rangnath Dhanvijay , Sudhir Madhav Patil “ Sugarcane Disease Detection Using CNN-Deep Learning Method: An Indian Perspective” Universal Journal of Agricultural Research 11(1): 80-97, 2023.
Md. Manowarul Islam , Md Abdul Ahad Adil , Md. Alamin Talukder , Md. Khabir Uddin Ahamed , Mdshraf Uddin , Md.Kamran Hasan , Selina Sharmin , Md.Mahbubur Rahman , Sumon Kumar Debnath” DeepCrop: Deep learning-based crop disease prediction with web application” Journal of Agriculture and Food Research Volume 14, December 2023, 10076
Shallu Sharma, Sumit Kumar “The Xception model: A potential feature extractor in breast cancer histology images classification” ICT Express,Volume 8, Issue 1, March 2022, Pages 101-108.
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