2021
DOI: 10.3389/fpls.2021.786702
|View full text |Cite
|
Sign up to set email alerts
|

Crop Agnostic Monitoring Driven by Deep Learning

Abstract: Farmers require diverse and complex information to make agronomical decisions about crop management including intervention tasks. Generally, this information is gathered by farmers traversing their fields or glasshouses which is often a time consuming and potentially expensive process. In recent years, robotic platforms have gained significant traction due to advances in artificial intelligence. However, these platforms are usually tied to one setting (such as arable farmland), or algorithms are designed for a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
29
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
1

Relationship

1
6

Authors

Journals

citations
Cited by 30 publications
(29 citation statements)
references
References 39 publications
0
29
0
Order By: Relevance
“…They were able to count strawberries with a MAPE of 3%; although their conditions were more favorable with fewer occlusions and a larger distance between individual fruits. In similar environment conditions as ours, Halstead et al (2021) developed and evaluated another similar tracking system to count bell peppers in individual plants, and achieved a MAPE of 4.1%. Their data also contained clutter and de-leafed areas of plants, but, since bell peppers do not grow in cluster like tomatoes, the distance between individual fruits is more favorable toward tracking.…”
Section: Discussionmentioning
confidence: 89%
See 3 more Smart Citations
“…They were able to count strawberries with a MAPE of 3%; although their conditions were more favorable with fewer occlusions and a larger distance between individual fruits. In similar environment conditions as ours, Halstead et al (2021) developed and evaluated another similar tracking system to count bell peppers in individual plants, and achieved a MAPE of 4.1%. Their data also contained clutter and de-leafed areas of plants, but, since bell peppers do not grow in cluster like tomatoes, the distance between individual fruits is more favorable toward tracking.…”
Section: Discussionmentioning
confidence: 89%
“…MOT methods have been recently used in agro-food robotic systems (Halstead et al, 2018;Kirk et al, 2021;Smitt et al, 2021;Halstead et al, 2021). Nevertheless, their objectives were to perform specific tasks like plant phenotyping and monitoring, and were not aiming for a generic representation to improve the capabilities of an autonomous robotic system in tasks like harvesting and crop maintenance.…”
Section: Introductionmentioning
confidence: 99%
See 2 more Smart Citations
“…From the early work of Nuske et al [5] and even the more recent deep convolutional neural network (DCNN) approach of Sa et al [6]. It has only been recent work that highlighted the potential of spatial and temporal cues as a rich source of information to improve the outputs and classification results of DCNNs [7]- [9] applied to agriculture. Despite these advances, it remains an open question about how best to combine the spatial and temporal Fig.…”
Section: Introductionmentioning
confidence: 99%