Insect detection in sticky trap images of tomato crops using machine learning

As climate change, biodiversity loss, and biological invaders are all on the rise, the significance of conservation and pest management initiatives cannot be stressed. Insect traps are frequently used in projects to discover and monitor insect populations, assign management and conservation strategies, and assess the effectiveness of treatment. This paper assesses the application of YOLOv5 for detecting insects in yellow sticky traps using images collected from insect traps in Portuguese tomato plantations, acquired under open field conditions. Furthermore, a sliding window approach was used to minimize insect detection duplicates in a non-complex way. This article also contributes to event forecasting in agriculture fields, such as diseases and pests outbreak, by obtaining insect related metrics that can be further analyzed and combined with other data extracted from the crop fields, contributing to smart farming and precision agriculture. The proposed method achieved good results when compared to related works, reaching 94.4% for mAP_0.5, with a precision and recall of 88% and 91%, respectively, using YOLOv5x.info:eu-repo/semantics/publishedVersio

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

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

Integrated grain storage - technology transfer for organic farming

The attached document is the final report of the Defra Project OF0176. Demand for organic grain continues to grow and currently in the UK much has to be imported to satisfy the market. Expansion of UK production, together with continued imports, will see an increase in the need for effective grain storage using organic methods to preserve the quality of this valuable commodity. The objectives of this project were to conduct a desk study review to: 1) Highlight the main grain storage problems experienced by organic growers in the UK. 2) Bring together in a more convenient form technical information already available that will enable growers to tackle these problems more successfully. 3) Identify areas of current storage technology requiring a minimum of modification to ensure a smooth transfer from the conventional to the organic sector. 4) Identify areas where appropriate organic alternatives to conventional storage procedures do not exist that will require further research and development to find replacements. The following have been identified as areas requiring further research and development that are likely to provide important opportunities for improving the storage of organic grain: • Store structure cleaning, including the effectiveness of vacuum cleaning and steam treatments, and the feasibility of using diatomaceous earths to control invertebrate pests. • Energy efficient drying and cooling systems, and consider the use of renewable energy sources including solar and wind power or the processing of waste products and energy crops. • More effective invertebrate pest monitoring through improvements in sampling strategies, together with enhanced trap design and the incorporation of lures. • Disinfestation using grain cleaners and hot air driers. • Biological control for store structure treatments. This will require work to assess the effectiveness of naturally occurring biological control agents to treat residual infestations hidden in empty grain store structures. Strategies will need to be developed to encourage the development and conservation of beneficial invertebrates in stores, including the possible use of natural semiochemicals to manipulate their behaviour. • Biological control for surface infestations of grain. As well as looking at the practicality of using top-dressing or bait trap application techniques to control grain surface infestations in cooled bins, there is a need to develop effective methods for the removal of beneficial invertebrates from the grain immediately before it is marketed. A more detailed Executive Summary can be found at the top of the main report

Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Images

This paper aims at developing an automatic algorithm for moth recognition from trap images in real-world conditions. This method uses our previous work for detection [1] and introduces an adapted classification step. More precisely, SVM classifier is trained with a multi-scale descriptor, Histogram Of Curviness Saliency (HCS). This descriptor is robust to illumination changes and is able to detect and to describe the external and the internal contours of the target insect in multi-scale. The proposed classification method can be trained with a small set of images. Quantitative evaluations show that the proposed method is able to classify insects with higher accuracy (rate of 95.8%) than the state-of-the art approaches

Machine vision detection of pests, diseases, and weeds: A review

Most of mankind’s living and workspace have been or going to be blended with smart technologies like the Internet of Things. The industrial domain has embraced automation technology, but agriculture automation is still in its infancy since the espousal has high investment costs and little commercialization of innovative technologies due to reliability issues. Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field. Early statistics on crop health can hasten prevention strategies such as pesticide, fungicide applications to reduce the pollution impact on water, soil, and air ecosystems. This paper condenses the proposed machine vision relate research literature in agriculture to date to explore various pests, diseases, and weeds detection mechanisms

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