Detecting Invasive Insects with Unmanned Aerial Vehicles

A key aspect to controlling and reducing the effects invasive insect species have on agriculture is to obtain knowledge about the migration patterns of these species. Current state-of-the-art methods of studying these migration patterns involve a mark-release-recapture technique, in which insects are released after being marked and researchers attempt to recapture them later. However, this approach involves a human researcher manually searching for these insects in large fields and results in very low recapture rates. In this paper, we propose an automated system for detecting released insects using an unmanned aerial vehicle. This system utilizes ultraviolet lighting technology, digital cameras, and lightweight computer vision algorithms to more quickly and accurately detect insects compared to the current state of the art. The efficiency and accuracy that this system provides will allow for a more comprehensive understanding of invasive insect species migration patterns. Our experimental results demonstrate that our system can detect real target insects in field conditions with high precision and recall rates.Comment: IEEE ICRA 2019. 7 page

Study of Interaction Between Mexican Free-tailed Bats (Tadarida Brasiliensis) and Moths and Counting Moths in a Real Time Video

Brazilian free tailed bats (Tadarida brasiliensis) are among the most abundant and widely distributed species in the southwestern United States in the summer. Because of their high metabolic needs and diverse diets, bats can impact the communities in which they live in a variety of important ways. The role of bats in pollination, seed dispersal and insect control has been proven to be extremely significant. Due to human ignorance, habitat destruction, fear and low reproductive rates of bats, there is a decline in bat populations. T.brasiliensis eats large quantities of insects but is not always successful in prey capture. In the face of unfavorable foraging condition bats reduce energy expenditure by roosting. By studying the interaction between bats and adults insects along with the associated energetics, we estimate the pest control provided by bats in agro-ecosystems to help understand their ecological importance. To visualize the interaction between bats and adult insects, a simulator has been designed. This simulator is based upon an individual based modeling approach. Using the simulator, we investigated the effect of insect densities and their escape response on the foraging pattern of bats. Traditionally synthetic pesticides were used to control pest population. But recently the use of transgenic crops has become widespread because of the benefits such as fewer pesticide applications and increased yield for growers. To study the effect of these transgenic crops on moth densities and subsequently on bats foraging activity, videos were recorded in the fields at Texas. To count the moths in the videos, we utilized image segmentation techniques such as thresholding and connected component labeling. Accuracy up to 90% has been achieved using these techniques

Mogućnost monitoring leta D.v. virgifera obradom slike sa feromonske klopke pomoću Raspberry Pi uređaja

Necessity for seasonal monitoring of economically most important pests in different crops, increase input costs of their surveillance. In maize fields, Western corn rootworm - WCR (Diabrotica virgifera sp. virgifera) is economically the most important species and presents a limiting factor for production of maize in continuous cultivation. Its occurrence is usually monitored with pheromone traps. However, as previously mentioned they are time and money consuming due to constant need for field inspections. Since in research projects, finances predefined for weekly inspection of traps are most often non-eligible, there is a need for developing a novel approach for pest monitoring. The use of IT technologies along with commercially available pheromone traps could provide precise information about the situation in traps without frequent field inspections. Also, they are easy to maintain, manipulate and require minimum costs. This work aimed to assess the potential use and the precision of a sensor device with camera, in monitoring the WCR flight on pheromone traps. Sensor device equipped with small camera can capture images of a pheromone trap sticky base and transfer them to a remote server for review, storage and analysis. The main idea in this paper is to present a system that uses a method based on analysis of the image variations of the pheromone pest traps, performed on devices placed in their vicinity which means that sending every image to the server is avoided. In this way, information about the variations in pheromone traps can be found in one location without unnecessarily sending the same images to the server. The obtained results indicate that the proposed method for monitoring the variations of the number of caught specimens on sticky surfaces of pheromone traps, based on the variations of the dark surface on the images, can be a reliable tool in further work.Neophodnost sezonskog monitoringa ekonomski najznačajnih štetočina u različitim usevima uzrokuje rast ulaznih troškova u poslovima nadzora njihove pojave. U usevu kukuruza, kukuruzna zlatica (Diabrotica virgifera sp. virgifera) je ekonomski najznačajnija štetočina i predstavlja ograničavajući faktor proizvodnje u monokulturi. Brojnost i pojava ove vrste se najčešće prati feromonskim klopkama. Međutim, kao što je napomenuto, njihova primena iziskuje dosta vremena i novca, usled konstantne potrebe za poljskim osmatranjima i obilascima klopki. U istraživačkim projektima sredstva predviđena za nedeljne preglede klopki su veoma često neprihvatljiv deo budžeta, što nameće potebu za razvojem novog pristupa monitoringu štetočina. Upotreba IT tehnologija uporedo sa komercijalno dostupnim feromonskim klopkama omogućava precizne informacije o stanju na klopkama, uz manje terenskih izlazaka, jednostavnost i niske troškove održavanja i manipulacije. Cilja rada je bio procena mogućnosti upotrebe i preciznosti senzorskih uređaja sa kamerom u monitoringu leta kukuruzne zlatice na fero-klopkama. Pomoću senzorskih uređaja opremljenih malim kamerama mogu se snimiti slike na mestu feromonskih klopki i proslediti do udaljenog servera za pregled, skladištenje i analizu. Ideja u ovom radu je prikaz sistema koji koristi metodu promene zauzetosti površine prilikom analize slike koja se izvršava na uređaju posredniku postavljenog pre servera. Na taj način se informacije o promeni brojnosti insekata u klopci mogu saznati na jednom lokalitetu bez nepotrebnog slanja istovetnih slika na server. Dobijeni rezultati ukazuju da se predloženi metod praćenja promene brojnosti na bazi promene površine prisustva tamnih polja (uhvaćenih insekata na lepljivoj površini feromonske klopke) može koristiti kao pouzdan alat u daljem radu

ChatGPT in the context of precision agriculture data analytics

In this study we argue that integrating ChatGPT into the data processing pipeline of automated sensors in precision agriculture has the potential to bring several benefits and enhance various aspects of modern farming practices. Policy makers often face a barrier when they need to get informed about the situation in vast agricultural fields to reach to decisions. They depend on the close collaboration between agricultural experts in the field, data analysts, and technology providers to create interdisciplinary teams that cannot always be secured on demand or establish effective communication across these diverse domains to respond in real-time. In this work we argue that the speech recognition input modality of ChatGPT provides a more intuitive and natural way for policy makers to interact with the database of the server of an agricultural data processing system to which a large, dispersed network of automated insect traps and sensors probes reports. The large language models map the speech input to text, allowing the user to form its own version of unconstrained verbal query, raising the barrier of having to learn and adapt oneself to a specific data analytics software. The output of the language model can interact through Python code and Pandas with the entire database, visualize the results and use speech synthesis to engage the user in an iterative and refining discussion related to the data. We show three ways of how ChatGPT can interact with the database of the remote server to which a dispersed network of different modalities (optical counters, vibration recordings, pictures, and video), report. We examine the potential and the validity of the response of ChatGPT in analyzing, and interpreting agricultural data, providing real time insights and recommendations to stakeholdersComment: 33 pages, 21 figure

A computer vision approach to monitoring the activity and well-being of honeybees

Honeybees, in their role as pollinators, are vital to both agriculture and the wider ecosystem. However, they have experienced a serious decline across much of the world over recent years. Monitoring their well-being, and taking appropriate action if that is in jeopardy, has thus become a matter of great importance. In this paper, we present an approach based on computer vision to monitor bee activity and motion in the vicinity of an entrance/exit to a hive, including identifying and counting the number of bees approaching or leaving the hive in a given image frame or sequence of image frames

Application of Digital Particle Image Velocimetry to Insect Motion: Measurement of Incoming, Outgoing, and Lateral Honeybee Traffic

The well-being of a honeybee (Apis mellifera) colony depends on forager traffic. Consistent discrepancies in forager traffic indicate that the hive may not be healthy and require human intervention. Honeybee traffic in the vicinity of a hive can be divided into three types: incoming, outgoing, and lateral. These types constitute directional traffic, and are juxtaposed with omnidirectional traffic where bee motions are considered regardless of direction. Accurate measurement of directional honeybee traffic is fundamental to electronic beehive monitoring systems that continuously monitor honeybee colonies to detect deviations from the norm. An algorithm based on digital particle image velocimetry is proposed to measure directional traffic. The algorithm uses digital particle image velocimetry to compute motion vectors, analytically classifies them as incoming, outgoing, or lateral, and returns the classified vector counts as measurements of directional traffic levels. Dynamic time warping is used to compare the algorithm’s omnidirectional traffic curves to the curves produced by a previously proposed bee motion counting algorithm based on motion detection and deep learning and to the curves obtained from a human observer’s counts on four honeybee traffic videos (2976 video frames). The currently proposed algorithm not only approximates the human ground truth on par with the previously proposed algorithm in terms of omnidirectional bee motion counts but also provides estimates of directional bee traffic and does not require extensive training. An analysis of correlation vectors of consecutive image pairs with single bee motions indicates that correlation maps follow Gaussian distribution and the three-point Gaussian sub-pixel accuracy method appears feasible. Experimental evidence indicates it is reasonable to treat whole bees as tracers, because whole bee bodies and not parts thereof cause maximum motion. To ensure the replicability of the reported findings, these videos and frame-by-frame bee motion counts have been made public. The proposed algorithm is also used to investigate the incoming and outgoing traffic curves in a healthy hive on the same day and on different days on a dataset of 292 videos (216,956 video frames)

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Automated computed tomography based parasitoid detection in mason bee rearings

In recent years, insect husbandry has seen an increased interest in order to supply in the production of raw materials, food, or as biological/environmental control. Unfortunately, large insect rearings are susceptible to pathogens, pests and parasitoids which can spread rapidly due to the confined nature of a rearing system. Thus, it is of interest to monitor the spread of such manifestations and the overall population size quickly and efficiently. Medical imaging techniques could be used for this purpose, as large volumes can be scanned non-invasively. Due to its 3D acquisition nature, computed tomography seems to be the most suitable for this task. This study presents an automated, computed tomography-based, counting method for bee rearings that performs comparable to identifying all Osmia cornuta cocoons manually. The proposed methodology achieves this in an average of 10 seconds per sample, compared to 90 minutes per sample for the manual count over a total of 12 samples collected around lake Zurich in 2020. Such an automated bee population evaluation tool is efficient and valuable in combating environmental influences on bee, and potentially other insect, rearings