Monitoring pest insect traps by means of low-power image sensor technologies

Monitoring pest insect populations is currently a key issue in agriculture and forestry protection. At the farm level, human operators typically must perform periodical surveys of the traps disseminated through the field. This is a labor-, time- and cost-consuming activity, in particular for large plantations or large forestry areas, so it would be of great advantage to have an affordable system capable of doing this task automatically in an accurate and a more efficient way. This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application. Our autonomous monitoring system will be able to cover large areas with very low energy consumption. This issue would be the main key point in our study; since the operational live of the overall monitoring system should be extended to months of continuous operation without any kind of maintenance (i.e., battery replacement). The images delivered by image sensors would be time-stamped and processed in the control station to get the number of individuals found at each trap. All the information would be conveniently stored at the control station, and accessible via Internet by means of available network services at control station (WiFi, WiMax, 3G/4G, etc.). © 2012 by the authors; licensee MDPI, Basel, Switzerland.This work was partially funded by Ministry of Education and Science grants CTM2011-29691-C02-01, TIN2011-28435-C03-01 and TIN2011-27543-C03-03.López ., O.; Martinez Rach, MO.; Migallon ., H.; Pérez Malumbres, MJ.; Bonastre Pina, AM.; Serrano Martín, JJ. (2012). Monitoring pest insect traps by means of low-power image sensor technologies. Sensors. 12(11):15801-15819. doi:10.3390/s121115801S15801158191211Shelton, A. M., & Badenes-Perez, F. R. (2006). CONCEPTS AND APPLICATIONS OF TRAP CROPPING IN PEST MANAGEMENT. Annual Review of Entomology, 51(1), 285-308. doi:10.1146/annurev.ento.51.110104.150959Jiang, J.-A., Tseng, C.-L., Lu, F.-M., Yang, E.-C., Wu, Z.-S., Chen, C.-P., … Liao, C.-S. (2008). A GSM-based remote wireless automatic monitoring system for field information: A case study for ecological monitoring of the oriental fruit fly, Bactrocera dorsalis (Hendel). Computers and Electronics in Agriculture, 62(2), 243-259. doi:10.1016/j.compag.2008.01.005http://www.memsic.comAl-Saqer. (2011). Red Palm Weevil (Rynchophorus Ferrugineous, Olivier) Recognition by Image Processing Techniques. American Journal of Agricultural and Biological Sciences, 6(3), 365-376. doi:10.3844/ajabssp.2011.365.376http://www.ti.com/lit/ds/symlink/cc1110f32.pdfhttp://www.comedia.com.hkOliver, J., & Perez Malumbres, M. (2008). On the Design of Fast Wavelet Transform Algorithms With Low Memory Requirements. IEEE Transactions on Circuits and Systems for Video Technology, 18(2), 237-248. doi:10.1109/tcsvt.2007.91396

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

Introduction to Robotics Agriculture in Pest Control: A Review

Agriculture is one of the latest industries that uses robotic technologies. Cultivation of crops with high yield and quality can be enhanced when technological sustenance is involved. Pests are nuisance and cannot be completely eliminated, but with effective control and management. damages caused by pests could be minimized below economic threshold. Automation in agriculture is stable and accurate and is mainly incorporated in mechanized farming system. However its numerous application in different agricultural practices is not well noticed. Hence this paper attempts to provide profound awareness on robotic technology in agriculture. Robots could have a specific or multiple functions and, most commonly, they are made up of five basic components; sensors, effectors, actuators, controller and arms. Use of automation in weeding, weed mapping, micro spraying, seeding, irrigation and harvesting are progressions which promote sustainable agriculture and food security. In future, solar robots with battery inverter may be invented

Automated detection and monitoring of grain beetles using a “smart” pitfall trap: Poster

A smart, electronic, modified pitfall trap, for automatic detection of adult beetle pests inside the grain mass is presented. The whole system is equipped with optoelectronic sensors to guard the entrance of the trap in order to detect, time-stamp, and GPS tag the incoming insect. Insect counts as well as environmental parameters that correlate with insect’s population development are wirelessly transmitted to a central monitoring agency in real time, are visualized and streamed to statistical methods to assist effective control of grain pests. The prototype trap was put in a large plastic barrel (120lt) with 80kg maize. Adult beetles of various species were collected from laboratory rearings and transferred to the experimental barrel. Caught beetle adults were checked and counted after 24h and were compared with the counts from the electronic system. Results from the evaluation procedure showed that our system is very accurate, reaching 98-99% accuracy on automatic counts compared with real detected numbers of adult beetles inside the trap. In this work we emphasize on how the traps can be selforganized in networks that collectively report data at local, regional, country, continental, and global scales using the emerging technology of the Internet of Things (IoT). We argue that smart traps communicating through IoT to report in real-time the level of the pest population from the grain mass straight to a human controlled agency can, in the very near future, have a profound impact on the decision making process in stored grain protection.A smart, electronic, modified pitfall trap, for automatic detection of adult beetle pests inside the grain mass is presented. The whole system is equipped with optoelectronic sensors to guard the entrance of the trap in order to detect, time-stamp, and GPS tag the incoming insect. Insect counts as well as environmental parameters that correlate with insect’s population development are wirelessly transmitted to a central monitoring agency in real time, are visualized and streamed to statistical methods to assist effective control of grain pests. The prototype trap was put in a large plastic barrel (120lt) with 80kg maize. Adult beetles of various species were collected from laboratory rearings and transferred to the experimental barrel. Caught beetle adults were checked and counted after 24h and were compared with the counts from the electronic system. Results from the evaluation procedure showed that our system is very accurate, reaching 98-99% accuracy on automatic counts compared with real detected numbers of adult beetles inside the trap. In this work we emphasize on how the traps can be selforganized in networks that collectively report data at local, regional, country, continental, and global scales using the emerging technology of the Internet of Things (IoT). We argue that smart traps communicating through IoT to report in real-time the level of the pest population from the grain mass straight to a human controlled agency can, in the very near future, have a profound impact on the decision making process in stored grain protection

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190

Towards a multisensor station for automated biodiversity monitoring

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution. (C) 2022 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie

Sensors in agriculture and forestry

Agriculture and Forestry are two broad and promising areas demanding technological solutions with the aim of increasing production or accurate inventories for sustainability while the environmental impact is minimized by reducing the application of agro-chemicals and increasing the use of environmental friendly agronomical practices. In addition, the immediate consequence of this “trend” is the reduction of production costs. Sensors-based technologies provide appropriate tools to achieve the above mentioned goals. The explosive technological advances and development in recent years enormously facilitates the attainment of these objectives removing many barriers for their implementation, including the reservations expressed by the farmers themselves. Precision Agriculture is an emerging area where sensor-based technologies play an important role.RHEA project [42], which is funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement NO.245986, which has been the platform for the two international conferences on Robotics and associated High-technologies and Equipment mentioned above.Peer Reviewe

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

Improving field management by machine vision - a review

Growing population of people around the world and thus increasing demand to food products as well as high tendency for declining the cost of operations and environmental preserving cares intensify inclination toward the application of variable rate systems for agricultural treatments, in which machine vision as a powerful appliance has been paid vast attention by agricultural researchers and farmers as this technology consumers. Various applications have introduced for machine vision in different fields of agricultural and food industry till now that confirms the high potential of this approach for inspection of different parameters affecting productivity. Computer vision has been utilized for quantification of factors affecting crop growth in field; such as, weed, irrigation, soil quality, plant nutrients and fertilizers in several cases. This paper presents some of these successful applications in addition to representing an introduction to machine vision

Recognition and Early Stage Detection of <em>Phytophthora</em> in a Crop Farm Using IoT

Detection of agricultural plant pests is seen as one of the farmers’ problems. Automated Pest Detection Machine enables early detection of crop insects with advanced computer vision and image recognition. Innovative research in the field of agriculture has demonstrated a new direction by Internet of Things (IoT). IoT needs to be widely experienced at the early stage, so that it is widely used in different farming applications. It allows farmers increase their crop yield with reduced time and greater precision. For the past decade, climate change and precipitation have been unpredictable. Due to this, many Indian farmers are adopting smart methods for environment known as intelligent farming. Smart farming is an automated and IOT-based information technology (Internet of Things). In all wireless environments IOT is developing quickly and widely. The Internet of Things helps to monitor agricultural crops and thus quickly and effectively increase farmers’ income. This paper presents a literature review on IoT devices for recognizing and detecting insects in crop fields. Different types of framework/models are present which are explaining the procedure of insect detection