• No results found

Mid to Late Season Weed Detection in Soybean Production Fields Using Unmanned Aerial Vehicle and Machine Learning

N/A
N/A
Protected

Academic year: 2021

Share "Mid to Late Season Weed Detection in Soybean Production Fields Using Unmanned Aerial Vehicle and Machine Learning"

Copied!
125
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Figure 2.1. Study area at South Central Agricultural Laboratory in Clay Center, NE
Figure 2.2. The DJI Matrice 600 pro UAV platform with Zenmuse X5R sensor used in  this study
Figure 2.3. Flowchart showing data annotation, feature extraction, training of different  machine learning models and convolutional neural network, comparison of the
Figure 2.4. Boxplot of the distribution of the “background” class including soybean and  soil pixels and the “weed” class in blue, green and red bands
+7

References

Related documents

$1000 and a complimentary letter to Mary Patten for her heroic work in bringing the fast sailing clipper ship safely around the dangerous Cape Horn?. When Mary and Captain

We also deal with the question whether the inferiority of the polluter pays principle in comparison to the cheapest cost avoider principle can be compensated

Comments This can be a real eye-opener to learn what team members believe are requirements to succeed on your team. Teams often incorporate things into their “perfect team

• Our goal is to make Pittsburgh Public Schools First Choice by offering a portfolio of quality school options that promote high student achievement in the most equitable and

The largest transactions in the third quarter of 2015 on the Polish M&A market were the acquisition of PKP Energetyka SA by the CVC Capital Partners and the acquisition of

ter mean to the prototypes computed from the true labels of all the samples. Similar to the semi-supervised scenario, we use a PN trained in the episodic mode as the feature

Political Parties approved by CNE to stand in at least some constituencies PLD – Partido de Liberdade e Desenvolvimento – Party of Freedom and Development ECOLOGISTA – MT –

During the thesis work, I measured six different parameters: the number of emergency processes, hash table entry number, caching replacement policy, cache entry