Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging

By Sandra Munera, C. Besada, Nuria Aleixos, P. Talens, A. Salvador, Da‐Wen Sun, Sergio Cubero, José Blasco. 2017

72 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biochemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/3c7262c8dc9fba8d37bea3a5011e78765440d1be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jaemyung Shin
2. Jian Jin
3. Md. Sultan Mahmud
4. Young K. Chang
5. Tanzeel U. Rehman
6. D. Saha
7. Rohit Sharma

Citations in the corpus

• Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, background): “Advantages of ML technology for agricultural crop are that it can be fairly accurate (Puerto et al., 2015), non-destructive (Munera et al., 2017) and yields consistent (Qureshi et al., 2017) results.”