Machine Learning-Based Plant Detection Algorithms to Automate Counting Tasks Using 3D Canopy Scans.

This study tested whether machine learning (ML) methods can effectively separate individual plants from complex 3D canopy laser scans as a prerequisite to analyzing particular plant features. For this, we scanned mung bean and chickpea crops with PlantEye (R) laser scanners. Firstly, we segmented the crop canopies from the background in 3D space using the Region Growing Segmentation algorithm. Then, Convolutional Neural Network (CNN) based ML algorithms were fine-tuned for plant counting. Application of the CNN-based (Convolutional Neural Network) processing architecture was possible only after we reduced the dimensionality of the data to 2D. This allowed for the identification of individual plants and their counting with an accuracy of 93.18% and 92.87% for mung bean and chickpea plants, respectively. These steps were connected to the phenotyping pipeline, which can now replace manual counting operations that are inefficient, costly, and error-prone. The use of CNN in this study was innovatively solved with dimensionality reduction, addition of height information as color, and consequent application of a 2D CNN-based approach. We found there to be a wide gap in the use of ML on 3D information. This gap will have to be addressed, especially for more complex plant feature extractions, which we intend to implement through further research.

Experimental Evaluation of the Availability of LoRaWAN Frequency Channels in the Czech Republic.

LoRaWAN communication allows you to create IoT (Internet of Things) solutions across many disciplines. A specific field of application is precision agriculture, which demands this technology mainly due to the fact that it is possible to create low power sensor devices with it. However, in densely populated areas, a lower success rate of message delivery can be observed on some communication channels. For example, this can have an impact on urban agriculture projects. After performing an experiment and analytical-statistical data processing using the Geographic Information System (GIS) tool ArcGIS Insights, it was shown that the success of message delivery on the basic LoRaWAN channel (868.3 MHz) is lower than for the others. Therefore, to ensure higher reliability and thus energy savings, it is appropriate to optimize the use of frequency channels.