A Reliable Approach for Pixel-Level Classification of Land usage from Spatio-Temporal Images

Purwar, Prateek; Rogotis, Savvas; Chatzipapadopoulus, Fotis; Kastanis, Iason

doi:10.1109/sds.2019.00004

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The classifier network produced a probability of being a particular crop for each input pixel. The details of the training for complete pipeline and obtained results can be found in [14].…”

Section: Classification Of Land Coveringmentioning

confidence: 99%

Data Analytics and Machine Learning

Järvinen

Siltanen

Kirschenbaum

2021

Big Data in Bioeconomy

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In this chapter we give an introduction to data analytics and machine learning technologies, as well as some examples of technologies used in the DataBio project. We start with a short intdroduction of basic concepts. We then describe how data analytics and machine learning markets have evolved. Next, we describe some basic technologies in the area. Finally, we describe how data analytics and machine learning were used in selected pilot cases of the DataBio project.

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“…The classifier network produced a probability of being a particular crop for each input pixel. The details of the training for complete pipeline and obtained results can be found in [14].…”

Section: Classification Of Land Coveringmentioning

confidence: 99%

Data Analytics and Machine Learning

Järvinen

Siltanen

Kirschenbaum

2021

Big Data in Bioeconomy

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“…The use of other types of neural networks can also be seen in the work by Barrero et al [29], who implemented weed detection in rice fields with perceptron-based networks. Purwar et al [30] applied Recurrent Neural Networks (RNN) in satellite images to classify crop plots.…”

Section: Introductionmentioning

confidence: 99%

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

et al. 2021

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This work presents an alternative method, referred to as Productivity Index or PI, to quantify the production of hydroponic tomatoes using computer vision and neural networks, in contrast to other well-known metrics, such as weight and count. This new method also allows the automation of processes, such as tracking of tomato growth and quality control. To compute the PI, a series of computational processes are conducted to calculate the total pixel area of the displayed tomatoes and obtain a quantitative indicator of hydroponic crop production. Using the PI, it was possible to identify objects belonging to hydroponic tomatoes with an error rate of 1.07%. After the neural networks were trained, the PI was applied to a full crop season of hydroponic tomatoes to show the potential of the PI to monitor the growth and maturation of tomatoes using different dosages of nutrients. With the help of the PI, it was observed that a nutrient dosage diluted with 50% water shows no difference in yield when compared with the use of the same nutrient with no dilution.

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A Reliable Approach for Pixel-Level Classification of Land usage from Spatio-Temporal Images

Cited by 2 publications

References 6 publications

Data Analytics and Machine Learning

Data Analytics and Machine Learning

Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing

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