&lt;italic&gt;In situ&lt;/italic&gt; detection of small-size insect pests sampled on traps using multifractal analysis

Xia, Chunlei

doi:10.1117/1.oe.51.2.027001

Cited by 25 publications

(29 citation statements)

References 31 publications

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“…Average accuracies of 78.1% [25], 96.3% [39], and 94.9% [40] were demonstrated by automatic monitoring systems counting the catches of the oriental fruit fly B. dorsalis. Other automated systems with image analysis technology also proved to be reliable in detecting mainly whiteflies and moths, with accuracies ranging from 70% to 100% [41][42][43][44][45]. The accuracy of our system is higher than almost all of the abovementioned monitoring systems.…”

Section: Resultsmentioning

confidence: 92%

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

2017

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Abstract:The concept of remote insect surveillance at large spatial scales for many serious insect pests of agricultural and medical importance has been introduced in a series of our papers. We augment typical, low-cost plastic traps for many insect pests with the necessary optoelectronic sensors to guard the entrance of the trap to detect, time-stamp, GPS tag, and-in relevant cases-identify the species of the incoming insect from their wingbeat. For every important crop pest, there are monitoring protocols to be followed to decide when to initiate a treatment procedure before a serious infestation occurs. Monitoring protocols are mainly based on specifically designed insect traps. Traditional insect monitoring suffers in that the scope of such monitoring: is curtailed by its cost, requires intensive labor, is time consuming, and an expert is often needed for sufficient accuracy which can sometimes raise safety issues for humans. These disadvantages reduce the extent to which manual insect monitoring is applied and therefore its accuracy, which finally results in significant crop loss due to damage caused by pests. With the term 'surveillance' we intend to push the monitoring idea to unprecedented levels of information extraction regarding the presence, time-stamping detection events, species identification, and population density of targeted insect pests. Insect counts, as well as environmental parameters that correlate with insects' population development, are wirelessly transmitted to the central monitoring agency in real time and are visualized and streamed to statistical methods to assist enforcement of security control to insect pests. In this work, we emphasize how the traps can be self-organized in networks that collectively report data at local, regional, country, continental, and global scales using the emerging technology of the Internet of Things (IoT). This research is necessarily interdisciplinary and falls at the intersection of entomology, optoelectronic engineering, data-science, and crop science and encompasses the design and implementation of low-cost, low-power technology to help reduce the extent of quantitative and qualitative crop losses by many of the most significant agricultural pests. We argue that smart traps communicating through IoT to report in real-time the level of the pest population from the field straight to a human controlled agency can, in the very near future, have a profound impact on the decision-making process in crop protection and will be disruptive of existing manual practices. In the present study, three cases are investigated: monitoring Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) using (a) Picusan and (b) Lindgren trap; and (c) monitoring various stored grain beetle pests using the stored-grain pitfall trap. Our approach is very accurate, reaching 98-99% accuracy on automatic counts compared with real detected numbers of insects in each type of trap.

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Section: Resultsmentioning

confidence: 92%

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

2017

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“…The accuracy of insect segmentation and computational time were evaluated across different levels of resolution. The precision and recall analysis were applied to determining accuracy of insect detection (Xia et al, 2012). Precision is defined as the proportion of correct segmentation of insect (true positives) to the total segmentation results and is expressed as True positive / (True positive + False positive).…”

Section: Resultsmentioning

confidence: 99%

“…Precision analysis (Table 2) was further performed to examine the correctness achieved by identification results; a high precision value indicates that the identification results contain a high percentage of correctly identified pests and a low percentage of false alarms as well (Xia et al, 2012), as stated above. False alarms frequently occurred in the identification results, affecting overall identification accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…For segmentation watershed was selected since the algorithm has been widely used in image processing including object detection (Boissard et al, 2008) and insect (i.e., whiteflies) body segmentation from field images (Xia et al, 2012). Direct application of the watershed segmentation, however, produces a number of unexpected regions in addition to the segmentation targets (Ng et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

“…Solis Sánchez et al (2009) utilized the geometric features (e.g., eccentricity, area size) to whitefly scouting by segmenting the insects from sticky trap images. Xia et al (2012) developed a multifractal dimension to detect whitefly in situ, which was robust to field noise such as unequal illumination changes and light reflections on trap surface.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic identification and counting of small size pests in greenhouse conditions with low computational cost

Xia

Chon

Ren

et al. 2015

Ecological Informatics

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a b s t r a c t a r t i c l e i n f oWe propose an automatic pest identification method suitable for large scale, long term monitoring for mobile or embedded devices in situ with less computational cost. A procedure of segmentation and image separation was devised to identify common greenhouse pests, whiteflies, aphid and thrips. Initially, the watershed algorithm was used to segment insects from the background (i.e., sticky trap) images. Color feature of the insects were subsequently extracted by Mahalanobis distance for identification of pest species. Accuracy and computational costs were evaluated across different image resolutions. The correlation of determination (R 2 ) between the proposed identification scheme and manual identification were high, showing 0.934 for whitefly, 0.925 for thrips, and 0.945 for aphids even with low resolution images. Comparing with the conventional methods, pests were efficiently identified with low computational cost. Optimal image resolution for species identification regarding long-term survey was discussed in practical aspect with less computational complexity.

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Scouting of Whiteflies in Tomato Greenhouse Environment Using Deep Learning

Turecek

Vařacha

Turečková

et al. 2021

Smart Innovation, Systems and Technologies

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This study shows the possibilities of how to replace tedious human labor -scouting of yellow sticky traps (YST) for whitefliesusing artificial cognitive vision, specifically deep convolutional network (CNN), as a part of the more complex system -BERABOT. The used CNN is the Faster R-CNN trained by deep transfer learning to substitute human scouting when the low whiteflies infection phase was specifically targeted. The training was conducted on pictures taken inside the heated and lighted tomato production greenhouse of "Bezdínek Farm" in Dolni Lutyne, Czechia. Used pictures were collected in a way suitable for future fully automated robotic applications in the BERABOT system. The achieved results were compared to the scouting results of a professional phytopathologist. The trained employee's scouting results against the professional phytopathologist accomplished root-mean-square error (RMSE) equal to 4.23 while the developed CNN model was evaluated to be 5.83. The results presented here open up new frontiers for further CNN model tuning leading to the potential in substituting an employee(S) in the future and make tomato production less expensive and less human labor dependent.

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<italic>In situ</italic> detection of small-size insect pests sampled on traps using multifractal analysis

Cited by 25 publications

References 31 publications

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

Automatic identification and counting of small size pests in greenhouse conditions with low computational cost

Scouting of Whiteflies in Tomato Greenhouse Environment Using Deep Learning

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