Assessment of Crop Yield Prediction Capabilities of CNN Using Multisource Data

The growing abundance of digitally available spatial, geological, and climatological data opens up new opportunities for agricultural data-based input–output modeling. In our study, we took a convolutional neural network model previously developed on Unmanned Aerial Vehicle (UAV) image data only and set out to see whether additional inputs from multiple sources would improve the performance of the model. Using the model developed in a preceding study, we fed field-specific data from the following sources: near-infrared data from UAV overflights, Sentinel-2 multispectral data, weather data from locally installed Vantage Pro weather stations, topographical maps from National Land Survey of Finland, soil samplings, and soil conductivity data gathered with a Veris MSP3 soil conductivity probe. Either directly added or encoded as additional layers to the input data, we concluded that additional data helps the spatial point-in-time model learn better features, producing better fit models in the task of yield prediction. With data of four fields, the most significant performance improvements came from using all input data sources. We point out, however, that combining data of various spatial or temporal resolution (i.e., weather data, soil data, and weekly acquired images, for example) might cause data leakage between the training and testing data sets when training the CNNs and, therefore, the improvement rate of adding additional data layers should be interpreted with caution.acceptedVersionPeer reviewe

Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier

In this paper, a novel cloud coverage assessment method for the Sentinel-2 data is presented. The method is based on the Random Forest classifier and the target values used in the training process are obtained by comparing the NDVI indexes calculated from the satellite and the UAV data. The developed method is shown to outperform the Sentinel Cloud Probability Mask (CLDPRB) and Scene Classification (SCL) data layers in detecting cloudy areas.acceptedVersionPeer reviewe

Requirements for training and evaluation dataset of network and host intrusion detection system

In the cyber domain, situational awareness of the critical assets is extremely important. For achieving comprehensive situational awareness, accurate sensor information is required. An important branch of sensors are Intrusion Detection Systems (IDS), especially anomaly based intrusion detection systems applying artificial intelligence or machine learning for anomaly detection. This millennium has seen the transformation of industries due to the developments in data based modelling methods. The most crucial bottleneck for modelling the IDS is the absence of publicly available datasets compliant to modern equipment, system design standards and cyber threat landscape. The predominant dataset, the KDD Cup 1999, is still actively used in IDS modelling research despite the expressed criticism. Other, more recent datasets, tend to record data only either from the perimeters of the testbed environment’s network traffic or from the effects that malware has on a single host machine. Our study focuses on forming a set of requirements for a holistic Network and Host Intrusion Detection System (NHIDS) dataset by reviewing existing and studied datasets within the field of IDS modelling. As a result, the requirements for state-of-the-art NHIDS dataset are presented to be utilised for research and development of NHIDS applying machine learning and artificial intelligence

Qualitative evaluation of dependency graph representativeness

Background: Enterprise application and open source software (OSS) platform and infrastructure projects are often today agile time-boxed projects. To enable project scaling, microservices software architecture (MSA) is considered to enable autonomous cross-functional teams. MSA results to loosely coupled services which communicate via well-designed APIs. Previous research on automated extraction of Microservice Dependency Graphs (MDGs) could provide means of reducing this documentation effort. Aims: The aim of the study was to look at the MDG representativeness of a Spinnaker OSS project micro-services-based software architecture and MDG, providing assessment of possibilities in using MDGs for documenting microservices-based software architectures. Method: The study uses a qualitative approach to evaluate the MDG representativeness of software architecture description. Evaluation is done through assessment of limitations, issues and future development possibilities. Results: MDG of Spinnaker OSS is extracted with an automation tool and contrasted to the software architecture as described on OSS project documentation. Compile-time MDG description and runtime focused documented software architecture lead to limitations in MDG rpresentativiness. Conclusions: Focusing on a particular OSS microservices project, the MDG extraction through static code analysis limits to compile-time information. Limitations in capturing inter-service communication at runtime to describe key architectural views of software architecture lead to a need to look for complementing approaches.publishedVersionPeer reviewe

Crop Yield Prediction Using Multitemporal UAV Data and Spatio-Temporal Deep Learning Models

publishedVersionPeer reviewe