A decision support system based on Bayesian modelling for pest management: Application to wireworm risk assessment in maize fields

Roche, Julien; Plantegenest, Manuel; Larroudé, Philippe; Thibord, Jean-Baptiste; Ronan, Le Cointe; Poggi, Sylvain

doi:10.1016/j.atech.2022.100162

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…al. [3], concrete means are needed to protect maize field plants from wireworms. Although there are several data-driven models used to evaluate wireworm infestation risk, such as the VFF-QC web application developed and used in Quebec, there is no widely available DSS for wireworm infestation risk assessment yet.…”

Section: Fig 5 Analysis Processmentioning

confidence: 99%

“…The use of this technology not only helps increase efficiency in decision-making, but also reduces the risk of crop loss due to pests and plant diseases. Research by Roche et al [3], has researched decision support system based on Bayesian modeling for pests management: Application for wireworm risk assessment in corn fields, but still not publicly accessible. In the context of SDGs Goals 2 and 15, this approach supports productive sustainable agriculture, reduces potential losses, and contributes to global food security.…”

Section: Introductionmentioning

confidence: 99%

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Decision support system as an element of web-based integrated pest control on cabbage plants

Wardhani,

Lestantyo,

Rahmi

2023

E3S Web Conf.

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Cabbage leaves are consumed as a healthy vegetable but the plant is very susceptible to attack by leaf caterpillars (P. xylostella). The residue from the periodic use of insecticides is very dangerous to human health. Hence Decision Support System (DSS) was developed which can be used as a reference when spraying insecticides is carried out. The system is intended to estimate the cabbage leaf damage based on web-based photo imagery that is used for detecting the percentage of pest attacks so that pesticides are only applied if the damage to the cabbage leaves is detected to be rather severe. Users are required to upload photos of cabbage plants to be detected. This research is carried out through literature studies, theoretical studies, data collection and location surveys of cabbage plant centers, statistical modeling, making a cabbage leaf hole display detection system with photo images, and making packages or libraries for models that have been developed, adding features that make it easier for users to run it. This developed system after passing the development process carries out 100% black box testing works according to the desired requirements. The implementation of pest experts and testing of this system gets an accuracy of about 87%, so the implementation is carried out on users. This developed system after going through the development process is carried out 100% blackbox testing to function according to the desired needs. The implementation of pest experts and testing of this system gets an accuracy of about 87%, so the implementation is carried out by the user. The results of this study have an analysis that can be stored in a database so that the utilization of recording analysis results can be directly stored in the database, can be printed, and can issue results from the Decision Support System for spraying cabbage pests.

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Section: Fig 5 Analysis Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision support system as an element of web-based integrated pest control on cabbage plants

Wardhani,

Lestantyo,

Rahmi

2023

E3S Web Conf.

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“…Agriculture DSS was also used to manage nutrients and irrigation on the farm [48]. A Bayesian model-based DSS was developed to predict the potential risk of wireworm [46]. The model used soil, field, and weather data to analyse the effects on the wireworm population.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management

Pun,

Neupane,

Koech

2023

J. Imaging

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Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task. This study developed a state-of-the-art deep learning model-based decision support tool to detect and estimate the nematode population. The decision support tool is integrated with the fast inferencing YOLOv5 model and used pretrained nematode weight to detect plant-parasitic nematodes (juveniles) and eggs. The performance of the YOLOv5-640 model at detecting RKN eggs was as follows: precision = 0.992; recall = 0.959; F1-score = 0.975; and mAP = 0.979. YOLOv5-640 was able to detect RKN eggs with an inference time of 3.9 milliseconds, which is faster compared to other detection methods. The deep learning framework was integrated into a user-friendly web application system to build a fast and reliable prototype nematode decision support tool (NemDST). The NemDST facilitates farmers/growers to input image data, assess the nematode population, track the population growths, and recommend immediate actions necessary to control nematode infestation. This tool has the potential for rapid assessment of the nematode population to minimise crop yield losses and enhance financial outcomes.

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A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem

et al. 2023

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A decision support system based on Bayesian modelling for pest management: Application to wireworm risk assessment in maize fields

Cited by 7 publications

References 26 publications

Decision support system as an element of web-based integrated pest control on cabbage plants

Decision support system as an element of web-based integrated pest control on cabbage plants

A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management

A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem

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