Authors : Lakshin Pathak; Mili Virani; Drashti Kansara

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/2yraeaak

Scribd : https://tinyurl.com/36xd6xsp

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN654

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Within the scope of the research, we put forward a technique of exactly confirming the distinctiveness of agricultural leaf pathologies with the assist of deep mastering algorithms and switch getting to know generation. We have pre-skilled models like VGG19, MobileNet, InceptionV3, EfficientNetB0, Simple CNN where we are seeking to increase the utility for the crop disorder type. Through searching at some metrics as cited Accuracy, Precision, Recall and F1 score for a better knowledge of a crop leaf photo category, we observe how each version performs. Our paper shows that artificial intelligence is fairly useful for the obligations of the automatic disease detection and switch mastering (as a method for reusing the existing understanding in the new software) is also beneficial. The contribution of this work to the development of reliable systems of save you sicknesses in production touches upon the rural exercise to achieve superiority fits into precision agriculture and sustainable farming. Future research ought to possibly include centered regions concerning a stability of datasets and stepped forward model interpretability which in turn will improve the fulfillment of these strategies in agricultural contexts.

Keywords : Crop Diseases, Leaf Diseases, Deep Learning, Transfer Learning, Classification.

References :

1. V. Sudha, U. Hemalatha, S. G. Shankar, and Thiyagarajan, “Lemon leaf disease detection using machine learning,” SSRG International Journal of Computer Science and Engineering, vol. 11, no. 1, pp. 1–10, 2024.
2. M. H. Ashmafee, T. Ahmed, S. Ahmed, M. B. Hasan, M. N. Jahan, and A. A. Rahman, “An efficient transfer learning-based approach for apple leaf disease classification,” in 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6, IEEE, 2023.
3. M. S. Arshad, U. A. Rehman, and M. M. Fraz, “Plant disease identifica- tion using transfer learning,” in 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1–5, 2021.
4. J. Kainat, S. Sajid Ullah, F. S. Alharithi, R. Alroobaea, S. Hussain, and S. Nazir, “Blended features classification of leaf-based cucumber disease using image processing techniques,” Complexity, vol. 2021, pp. 1–12, 2021.
5. P. Tm, A. Pranathi, K. SaiAshritha, N. B. Chittaragi, and S. G. Koolagudi, “Tomato leaf disease detection using convolutional neural networks,” in 2018 eleventh international conference on contemporary computing (IC3), pp. 1–5, IEEE, 2018.
6. K. Aravind, P. Raja, K. Mukesh, R. Aniirudh, R. Ashiwin, and Szczepanski, “Disease classification in maize crop using bag of fea- tures and multiclass support vector machine,” in 2018 2nd international conference on inventive systems and control (ICISC), pp. 1191–1196, IEEE, 2018.
7. P. Patil, N. Yaligar, and S. Meena, “Comparision of performance of classifiers-svm, rf and ann in potato blight disease detection using leaf images,” in 2017 IEEE international conference on computational intelligence and computing research (ICCIC), pp. 1–5, IEEE, 2017.