Deep Learning Applications in Agriculture: A Short Review

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Santos, L (Author) Other; Filipe Neves Santos (Author) FEUP; Oliveira, PM (Author) Other; Shinde, P (Author) Other

Conference proceedings International

Title: FOURTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, ROBOT 2019, VOL 1

Pages: 139-151

4th Iberian Robotics Conference (Robot) - Advances in Robotics

Porto, PORTUGAL, NOV 20-22, 2019

Indexing

ISI Web of Knowledge - 62 Citations

Scopus - 93 Citations

Other information

Authenticus ID: P-00R-CG4

DOI: 10.1007/978-3-030-35990-4_12

Abstract (EN): Deep learning (DL) incorporates a modern technique for image processing and big data analysis with large potential. Deep learning is a recent tool in the agricultural domain, being already successfully applied to other domains. This article performs a survey of different deep learning techniques applied to various agricultural problems, such as disease detection/identification, fruit/plants classification and fruit counting among other domains. The paper analyses the specific employed models, the source of the data, the performance of each study, the employed hardware and the possibility of real-time application to study eventual integration with autonomous robotic platforms. The conclusions indicate that deep learning provides high accuracy results, surpassing, with occasional exceptions, alternative traditional image processing techniques in terms of accuracy.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13