TY  - JOUR
AV  - none
SP  - 299
PB  - Springer Science and Business Media Deutschland GmbH
KW  - Computer vision; Deep learning; Fruits; Grading; Large dataset; Learning algorithms; Learning systems; Surveys; Vegetables
KW  -  'current; Deep learning; Detection estimation; Food industries; Fruit and vegetables; Fruit harvesting; Learning methods; Objects detection; Tools and techniques; Yield estimation
KW  -  Object detection
TI  - A Survey of Deep Learning Methods for Fruit and Vegetable Detection and Yield Estimation
SN  - 21976503
N2  - Computer vision has a great potential to deal with agriculture problems. It is crucial to utilize novel tools and techniques in the agriculture food industry. The focus of current studies is to automate the fruit harvesting, grading of fruits, fruit recognition, and identification of diseases in the agriculture domain using deep learning and computer vision. Integrating deep learning with computer vision facilitates the consistent, speedy and trustworthy classification of fruit and vegetables compared to the traditional machine learning algorithm. However, there are still some challenges, such as the need for expert farmers to develop large-scale datasets to recognize and identify the problems of agriculture production. This survey includes eighty papers relevant to deep learning and computer vision techniques in the agriculture field. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
EP  - 323
ID  - scholars17545
JF  - Studies in Big Data
VL  - 111
Y1  - 2022///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137578486&doi=10.1007%2f978-3-031-05752-6_19&partnerID=40&md5=2b5ee8b69c85a5e80debea316ec22606
A1  - Aslam, F.
A1  - Khan, Z.
A1  - Tahir, A.
A1  - Parveen, K.
A1  - Albasheer, F.O.
A1  - Ul Abrar, S.
A1  - Khan, D.M.
N1  - cited By 6
ER  -