A Machine Learning Platform for Multirotor Activity Training and Recognition

Rosa, Matthew De La; Chen, Yinong

doi:10.1109/isads45777.2019.9155812

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Currently, we manually photograph the tomato leaves. For future work, we could adopt the drone approach [36] to automatically and systematically photograph the tomato leaves based on the planned paths to extend our research to massive farming lands. Applying the methods to other agriculture products are also possible.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

Ponce

Cevallos

Espinosa

et al. 2021

JAIT

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Tomato crops are considered the most important agricultural products worldwide. However, the quality of tomatoes depends mainly on the nutrient levels. Visual inspection is made by farmers to anticipate the nutrient deficiency of the plants. Recently, precision agriculture has explored opportunities to automate nutrient level monitoring. Previous work has demonstrated that a convolutional neural network (CNN) is able to estimate low nutrients in tomato plants using images of their leaves. However, the performance of the CNN was not adequate. Thus, this work proposes a novel CNNbased classifier, namely CNN+AHN, for estimating low nutrients in tomato crops using an image of the tomato leaves. The CNN+AHN incorporates a set of convolutional layers as the feature extraction part, and a supervised learning method called artificial hydrocarbon network (AHN) as the dense layer. Different combinations of the architecture of CNN+AHN were examined. Experimental results showed that our best CNN+AHN classifier is able to estimate low nutrients in tomato plants with an accuracy of 95:57% and F1-score of 95:75%, outperforming the literature.

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Section: Discussionmentioning

confidence: 99%

Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

Ponce

Cevallos

Espinosa

et al. 2021

JAIT

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“…A flight path machine-learning application was developed using the drone in Fig. 10, which uses VIPLE and AWS AI services [19]. A guide dog application for vision impaired was developed using the BIOLOID robot in Fig.…”

Section: B Iot/robotics Programming In Viplementioning

confidence: 99%

Technologies Supporting Artificial Intelligence and Robotics Application Development

Chen

Luca

2021

JAIT

Self Cite

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Artificial intelligence (AI) and robotics have gone through three generations of development, from Turing test, logic theory machine, to expert system and self-driving car. In the third-generation today, AI and robotics have collaboratively been used in many areas in our society, including industry, business, manufacture, research, and education. There are many challenging problems in developing AI and robotics applications. We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies. In this inaugural issue, we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms. We present examples of such development environments created by both industry and academia. We also selected eight papers in the related areas to celebrate the foundation of this journal.

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“…Machine learning is a new paradigm of problem solving [3]. Reinforcement learning is an important machine learning method and applied many fields of intelligent control.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional obstacle avoidance for UAV based on reinforcement learning and RealSense

Ding

Yang

Wang

2020

J. eng.

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A Machine Learning Platform for Multirotor Activity Training and Recognition

Cited by 5 publications

References 12 publications

Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

Technologies Supporting Artificial Intelligence and Robotics Application Development

Three‐dimensional obstacle avoidance for UAV based on reinforcement learning and RealSense

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