Determine the population and occurrence from above

Our proposed methodology aims to develop an aerial-based approach for estimating common vole and field hamster occurrences. This methodology utilizes Unmanned Aerial Vehicles (UAVs), commonly known as drones , equipped with high-resolution imaging sensors and is powered by advanced evaluation algorithms based on deep machine learning techniques. Evaluating the efficiency and cost-effectiveness of the proposed aerial-based methodology is of outmost importance and will be rigorously evaluated in direct comparison with conventional ground-based methods.

Our proposed methodology aims to develop an aerial-based approach for estimating common vole and field hamster occurrences. This methodology utilizes Unmanned Aerial Vehicles (UAVs), commonly known as drones , equipped with high-resolution imaging sensors and is powered by advanced evaluation algorithms based on deep machine learning techniques. Evaluating the efficiency and cost-effectiveness of the proposed aerial-based methodology is of outmost importance and will be rigorously evaluated in direct comparison with conventional ground-based methods.

Conventional methodology

Surveying the occurrence and population of field hamsters and common voles are usually carried out using conventional field inspections. The execution of these field inspections differ between the two species. The survey of the field hamster population is carried out with a human chain that moves in a closed line across the suspected area and checks whether hamsters or inhabited burrows are present on the field. The use of a human chain is necessary to ensure that no burrow or hamster is overlooked.

On the other hand, common vole populations are determined using the so called Lochtretmethode (manually closing of burrows). This methodology involves closing the burrow entrances in up to four widely separated areas of at least 250 m² (16 x 16 meters) per infested field. After 24 hours, the second survey takes place, during which the reopened holes are counted. If 5 to 8 holes are opened (this corresponds to 80 to 120 mice per m²), the control guideline value is considered to have been reached on crops such as winter wheat and rapeseed. On other types of land such as grassland, the control guideline value is reached when 11 open holes are present. If the common vole infested area is known to be an occurrence area of the field hamster or if field hamsters (or their burrows) were encountered, it is necessary to carry out another survey aimed at the field hamster. Rodenticides and other countermeasures against an high population of common voles can only be carried out after establishing that no field hamsters are present.

Surveying the occurrence and population of field hamsters and common voles are usually carried out using conventional field inspections. The execution of these field inspections differ between the two species. The survey of the field hamster population is carried out with a human chain that moves in a closed line across the suspected area and checks whether hamsters or inhabited burrows are present on the field. The use of a human chain is necessary to ensure that no burrow or hamster is overlooked.

On the other hand, common vole populations are determined using the so called Lochtretmethode (manually closing of burrows). This methodology involves closing the burrow entrances in up to four widely separated areas of at least 250 m² (16 x 16 meters) per infested field. After 24 hours, the second survey takes place, during which the reopened holes are counted. If 5 to 8 holes are opened (this corresponds to 80 to 120 mice per m²), the control guideline value is considered to have been reached on crops such as winter wheat and rapeseed. On other types of land such as grassland, the control guideline value is reached when 11 open holes are present. If the common vole infested area is known to be an occurrence area of the field hamster or if field hamsters (or their burrows) were encountered, it is necessary to carry out another survey aimed at the field hamster. Rodenticides and other countermeasures against an high population of common voles can only be carried out after establishing that no field hamsters are present.

Project-specific methodology

Accurate population and occurrence monitoring of field hamsters and common vole is crucial for conservation efforts and pest control. Traditional methods are labor-intensive and time-consuming, limiting their applicability over large areas. The CRIFORA project proposes a novel approach that utilizes drone-based imagery and machine learning algorithms to automate the detection and differentiation of field hamster and common vole burrows. The proposed methodology employs drones equipped with high-resolution true color cameras and thermal microbolometersto capture detailed imagery of the terrain. Artificial intelligence and deep learning algorithms are then applied to analyze the acquired data, enabling the identification and classification of field hamster and field mouse burrows. Furthermore, the methodology is designed to output the geographical coordinates of each detected burrow, along with its classification as either a field hamster or common vole burrow.

The effectiveness of the proposed drone-based methodology is validated through a series of simultaneous field inspections. These field inspections are conducted strategically throughout the year and across diverse areas with varying conditions and crops, mirroring the aerial surveys. The objective of these field inspections is to determine the optimal times of the year for conducting aerial surveys to achieve the most accurate detection results.

Accurate population and occurrence monitoring of field hamsters and common vole is crucial for conservation efforts and pest control. Traditional methods are labor-intensive and time-consuming, limiting their applicability over large areas. The CRIFORA project proposes a novel approach that utilizes drone-based imagery and machine learning algorithms to automate the detection and differentiation of field hamster and common vole burrows. The proposed methodology employs drones equipped with high-resolution true color cameras and thermal microbolometersto capture detailed imagery of the terrain. Artificial intelligence and deep learning algorithms are then applied to analyze the acquired data, enabling the identification and classification of field hamster and field mouse burrows. Furthermore, the methodology is designed to output the geographical coordinates of each detected burrow, along with its classification as either a field hamster or common vole burrow.

The effectiveness of the proposed drone-based methodology is validated through a series of simultaneous field inspections. These field inspections are conducted strategically throughout the year and across diverse areas with varying conditions and crops, mirroring the aerial surveys. The objective of these field inspections is to determine the optimal times of the year for conducting aerial surveys to achieve the most accurate detection results.

Determine the population and occurrence from above

Determine the population and occurrence from above

Our proposed methodology aims to develop an aerial-based approach for estimating common vole and field hamster occurrences. This methodology utilizes Unmanned Aerial Vehicles (UAVs), commonly known as drones , equipped with high-resolution imaging sensors and is powered by advanced evaluation algorithms based on deep machine learning techniques. Evaluating the efficiency and cost-effectiveness of the proposed aerial-based methodology is of outmost importance and will be rigorously evaluated in direct comparison with conventional ground-based methods.

Our proposed methodology aims to develop an aerial-based approach for estimating common vole and field hamster occurrences. This methodology utilizes Unmanned Aerial Vehicles (UAVs), commonly known as drones , equipped with high-resolution imaging sensors and is powered by advanced evaluation algorithms based on deep machine learning techniques. Evaluating the efficiency and cost-effectiveness of the proposed aerial-based methodology is of outmost importance and will be rigorously evaluated in direct comparison with conventional ground-based methods.

Conventional methodology

Surveying the occurrence and population of field hamsters and common voles are usually carried out using conventional field inspections. The execution of these field inspections differ between the two species. The survey of the field hamster population is carried out with a human chain that moves in a closed line across the suspected area and checks whether hamsters or inhabited burrows are present on the field. The use of a human chain is necessary to ensure that no burrow or hamster is overlooked.

On the other hand, common vole populations are determined using the so called Lochtretmethode (manually closing of burrows). This methodology involves closing the burrow entrances in up to four widely separated areas of at least 250 m² (16 x 16 meters) per infested field. After 24 hours, the second survey takes place, during which the reopened holes are counted. If 5 to 8 holes are opened (this corresponds to 80 to 120 mice per m²), the control guideline value is considered to have been reached on crops such as winter wheat and rapeseed. On other types of land such as grassland, the control guideline value is reached when 11 open holes are present. If the common vole infested area is known to be an occurrence area of the field hamster or if field hamsters (or their burrows) were encountered, it is necessary to carry out another survey aimed at the field hamster. Rodenticides and other countermeasures against an high population of common voles can only be carried out after establishing that no field hamsters are present.

Surveying the occurrence and population of field hamsters and common voles are usually carried out using conventional field inspections. The execution of these field inspections differ between the two species. The survey of the field hamster population is carried out with a human chain that moves in a closed line across the suspected area and checks whether hamsters or inhabited burrows are present on the field. The use of a human chain is necessary to ensure that no burrow or hamster is overlooked.

On the other hand, common vole populations are determined using the so called Lochtretmethode (manually closing of burrows). This methodology involves closing the burrow entrances in up to four widely separated areas of at least 250 m² (16 x 16 meters) per infested field. After 24 hours, the second survey takes place, during which the reopened holes are counted. If 5 to 8 holes are opened (this corresponds to 80 to 120 mice per m²), the control guideline value is considered to have been reached on crops such as winter wheat and rapeseed. On other types of land such as grassland, the control guideline value is reached when 11 open holes are present. If the common vole infested area is known to be an occurrence area of the field hamster or if field hamsters (or their burrows) were encountered, it is necessary to carry out another survey aimed at the field hamster. Rodenticides and other countermeasures against an high population of common voles can only be carried out after establishing that no field hamsters are present.

Project-specific methodology

Accurate population and occurrence monitoring of field hamsters and common vole is crucial for conservation efforts. Traditional methods are labor-intensive and time-consuming, limiting their applicability over large areas. The CRIFORA project proposes a novel approach that utilizes drone-based imagery and machine learning algorithms to automate the detection and differentiation of field hamster and common vole burrows. The proposed methodology employs drones equipped with high-resolution true color cameras and thermal microbolometersto capture detailed imagery of the terrain. Artificial intelligence and deep learning algorithms are then applied to analyze the acquired data, enabling the identification and classification of field hamster and field mouse burrows. Furthermore, the methodology is designed to output the geographical coordinates of each detected burrow, along with its classification as either a field hamster or common vole burrow.

The effectiveness of the proposed drone-based methodology is validated through a series of simultaneous field inspections. These field inspections are conducted strategically throughout the year and across diverse areas with varying conditions and crops, mirroring the aerial surveys. The objective of these field inspections is to determine the optimal times of the year for conducting aerial surveys to achieve the most accurate detection results.

Accurate population and occurrence monitoring of field hamsters and common vole is crucial for conservation efforts. Traditional methods are labor-intensive and time-consuming, limiting their applicability over large areas. The CRIFORA project proposes a novel approach that utilizes drone-based imagery and machine learning algorithms to automate the detection and differentiation of field hamster and common vole burrows. The proposed methodology employs drones equipped with high-resolution true color cameras and thermal microbolometersto capture detailed imagery of the terrain. Artificial intelligence and deep learning algorithms are then applied to analyze the acquired data, enabling the identification and classification of field hamster and field mouse burrows. Furthermore, the methodology is designed to output the geographical coordinates of each detected burrow, along with its classification as either a field hamster or common vole burrow.

The effectiveness of the proposed drone-based methodology is validated through a series of simultaneous field inspections. These field inspections are conducted strategically throughout the year and across diverse areas with varying conditions and crops, mirroring the aerial surveys. The objective of these field inspections is to determine the optimal times of the year for conducting aerial surveys to achieve the most accurate detection results.