MASHINALI O‘QITISH MODELLARI UCHUN TASVIR DESKRIPTORLARI ASOSIDA YUQORI ANIQLIKDAGI O‘QUV TANLANMALARINI SHAKLLANTIRISH ALGORIMTLARI
DOI:
https://doi.org/10.65164/2mz2cs67Keywords:
tasvirlarga ishlov berish, xususiyatlarni ajratish, pomidor kasalliklari, mashinali o‘qitish, rang deskriptorlari, tekstura tahlili.Abstract
Mazkur tadqiqot ishi timsollarni tanib olish sohasida obyektni harakterlovchi xususiyatlarni ajratish algorimtlarini takomillashitirishga qaratilgan bo‘lib, mazkur ishda Kaggle platformasidagi “Pomidor kasalliklari” (PlantVillage) ma’lumotlar to‘plamidan 10 000 ta rasm ishlatilgan bo‘lib, ulardan 29 ta ma’lumot beruvchi xususiyatlardan iborat o‘quv to‘plami shakllantirilgan.Bu yondashuv issiqxona va ochiq maydonlarda ekinlarni erta tashxislashda va himoya choralarini prognozlashda muhim amaliy ahamiyatga ega.umotlar to‘plamidan 10 000 ta rasm ishlatilgan bo‘lib, ulardan 29 ta ma’lumot beruvchi xususiyatlardan iborat o‘quv to‘plami shakllantirilgan. Natijalar shuni ko‘rsatadiki, taklif qilingan xususiyatlarni saralash usuli modelning aniqlik koeffitsientini oshirish bilan birga hisoblash vaqtini sezilarli darajada tejaydi. Bu yondashuv issiqxona va ochiq maydonlarda ekinlarni erta tashxislashda va himoya choralarini prognozlashda muhim amaliy ahamiyatga ega.
References
1.
Ali H, LaliMI, NawazMZ, SharifM, Saleem BA (2017) Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Comput Electron Agric 138:92– 104. https://doi.org/10.1016/j.compag.2017.04.008
2.
Deng Y, Yu Y (2019) Self-feedback image retrieval algorithm based on annular color moments. EURASIP J Image Video Process 2019(1):1–13. https://doi.org/10.1186/s13640-018-0400-9
3.
Souza L, Ebigbo A, Probst A, Messmann H, Papa JP, Mendel R, Palm C (2018) Barrett’s Esophagus identification using color co-occurrence matrices. In: 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI), pp 166–173. https://doi.org/10.1109/sibgrapi. 2018.00028
4.
Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., Villa, P., Stroppiana, D., Boschetti, M., Goulart, L. R., Davis, C. E., and Dandekar, A. M. 2014. Advanced methods for plant disease detection. A review. Agron. Sustain. Dev. 35:1-25. https://doi.org/10.1007/s13593-014-0246-1 CrossrefWeb of ScienceGoogle Scholar
5.
Suhang Wang, Jiliang Tang, and Huan Liu, Feature selection, USA, 2017.
