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Received: February 1st, 2023 ; Accepted: April 25th, 2023 ; Published: May 10th, 2023 ; Correspondence: mikael.anakkala@helsinki.fi Interest in forage maize (Zea mays L.) cultivation for livestock feed has grown in northern conditions. In addition, it is important to develop methods and tools to monitor crop development and other characteristics of the crop. For these purposes UAVs are very efficient and versatile tools. UAVs can be equipped with a variety of sensors like lidar or different types of cameras. Several studies have been conducted where data collected by UAVs are used to estimate different crop properties like yield and biomass. In this research, a forage maize field experiment was studied to examine how well the aerial multispectral data correlated with the different properties of the vegetation. The field test site is located in Helsinki, Finland. A multispectral camera (MicaSense Rededge 3) was used to take images from five spectral bands (Red, Green, Blue, Rededge and NIR). All the images were processed with Pix4D software to generate orthomosaic images. Several vegetation indices were calculated from the five spectral bands. During the growing season, crop height, chlorophyll content, leaf area index (LAI), fresh and dry matter biomass were measured from the vegetation. From the five spectral bands, Rededge had the highest correlation with fresh biomass (R2 = 0.273). The highest correlation for a vegetation index was found between NDRE and chlorophyll content (R2 = 0.809). A multiple linear regression (MLR) model using selected spectral bands and vegetation indices as inputs showed high correlations with the field measurements.

Smart agriculture technologies are effective instruments for increasing farm sustainability and production. They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and efficiency. In this context, the detection of anomalies can help in the identification of observations that deviate from the norm. This paper proposes an adaptation of an ensemble anomaly detector called enhanced locally selective combination in parallel outlier ensembles (ELSCP). On this basis, we define an unsupervised data-driven methodology for smart-farming temporal data that is applied in two case studies. The first considers harvest data including combine-harvester Global Positioning System (GPS) traces. The second is dedicated to crop data where we study the link between crop state (damaged or not) and detected anomalies. Our experiments show that our methodology achieved interesting performance with Area Under the Curve of Precision-Recall (AUCPR) score of 0.972 in the combine-harvester dataset, which is 58.7% better than that of the second-best approach. In the crop dataset, our analysis showed that 30% of the detected anomalies could be directly linked to crop damage. Therefore, anomaly detection could be integrated in the decision process of farm operators to improve harvesting efficiency and crop health.

Received: January 30th, 2023 ; Accepted: April 8th, 2023 ; Published: May 3rd , 2023 ; Correspondence: ketija.bumbiere@rtu.lv Agriculture is one of the most energy-consuming sectors in the EU’s economy. Implementing sustainable agriculture to reduce GHG emissions and increase energy efficiency through energy management is a crucial strategy to tackle climate change. In this paper, the role of energy management in the agricultural sector is studied, and experiences from Europe and the world have been considered. Literature analysis regarding the chosen topic has been conducted, including the methodology of energy management plan development and its implementation in the case study of Latvia. Data from Latvia’s agricultural and other sectors have been analysed and compared. Latvia’s Inventory Report regarding GHG emissions in the agricultural sector was reviewed, and all emission sources in the agricultural sector were highlighted. The primary purpose of the study is to find out if energy management were introduced in an agricultural company, what would be the potential GHG emission, energy savings and additional advantages. Two companies working in Latvia were surveyed, and potential emission and energy consumption reduction measures in agriculture that would be applied to companies were developed. The research showed that by implementing the basic principles of energy management, it would be possible to reduce the average energy consumption by 17%. If measures are applied to reduce GHG emissions from agricultural companies, the average emissions would be reduced by 43%.