A Systematic Review of Pomegranate Fruit Disease Detection and Classification Using Machine Learning and Deep Learning Techniques
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9486-1_13
Reference17 articles.
1. Pal A, Kumar V (2023) AgriDet: Plant Leaf Disease severity classification using agriculture detection framework. Eng Appl Artif Intell 119:105754. ISSN: 0952-1976. https://doi.org/10.1016/j.engappai.2022.105754
2. Nirgude V, Rathi S (2021) A robust deep learning approach to enhance the accuracy of pomegranate fruit disease detection under real field conditions. J Exp Biol Agric Sci 9(6):863–870. https://doi.org/10.18006/2021.9(6).863.870
3. Nirmal MD, Jadhav P, Pawar S (2022) Pomegranate leaf disease classification using feature extraction and machine learning. In: 2022 3rd international conference on smart electronics and communication (ICO-SEC), Trichy, India, 2022, pp 619–626. https://doi.org/10.1109/ICOSEC54921.2022.9951907
4. Sharath DM, Akhilesh RMG, Arun Kumar S, Prathap C (2020) Disease detection in pomegranate using image processing. In: 2020 4th international conference on trends in electronics and informatics (ICOEI) (48184), Tirunelveli, India, 2020, pp 994–999. https://doi.org/10.1109/ICOEI48184.2020.9142972
5. Kantale P, Thakare S (2020) Pomegranate disease classification using Ada-Boost ensemble algorithm. Int J Eng Res Technol (IJERT) 09(09)
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