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 :
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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.