A knowledge-based approach to designing control strategies for agricultural pests

Agatz, Annika; Ashauer, Roman; Sweeney, Paul; Brown, Colin D.

doi:10.1016/j.agsy.2020.102865

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…In order to minimize the economic and environmental damages caused by these pests, several attempts have been made to predict pests [1,10]. These disease modeling attempts create several mathematical models that explain the occurrence and development of pests by various factors and use it to predict the occurrence and severity of pests [11,12], understand eases and pests reduce the yield and quality of crops, increase costs due to increased labor for pest treatment, the development of disease [13][14][15], It is used for assisting tactical decision making [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Lee,

Yun

2023

Plant Methods

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Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO2 concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention.

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Section: Introductionmentioning

confidence: 99%

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Lee,

Yun

2023

Plant Methods

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“…Due to its importance expressed into its high consumption worldwide (the global importance of potatoes is so great that FAO, the UN body for Agriculture and Food instituted 2008, as the "International Year of Potatoes"), it is essential to create more resistant varieties, contributing to an increase in productivity and thus minimizing hunger in several countries. The scarcity of these resistant varieties requires the use of pesticides to control pests and contribute to increase productivity [1,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Separations 2022, 9, x FOR PEER REVIEW 2 of 11 great that FAO, the UN body for Agriculture and Food instituted 2008, as the "International Year of Potatoes"), it is essential to create more resistant varieties, contributing to an increase in productivity and thus minimizing hunger in several countries. The scarcity of these resistant varieties requires the use of pesticides to control pests and contribute to increase productivity [1,3,4]. Depending on the degree of toxicity, each pesticide has a maximum permissible value of the application, which should not be exceeded to minimize the harmful effects on human health.…”

Section: Introductionmentioning

confidence: 99%

µQuEChERS Combined with UHPLC-PDA as a State-of-the-Art Analytical Approach for Quantification of Chlorpropham in Potato

Gomes

Perestrelo

2022

Separations

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Pesticides are chemicals used in agriculture to prevent insects, fungi, weeds, and other pests, from damaging crops. In addition, some types of pesticides are used after harvest as sprout suppressant agents help keeping the quality parameters of crops during storage. Nonetheless, its presence, even at trace levels, in food products is becoming a big challenge regarding human health. The current work aimed to develop and validate a sensitive and high-throughput analytical approach, based on a state-of-the-art microextraction technique—µQuEChERS, combined with ultra-high performance liquid chromatography equipped with a photodiode array detection system (UHPLC-PDA) to quantify isopropyl-N-(3-chlorophenyl) carbamate (chlorpropham), commonly used as efficient sprout suppressant stored potatoes, in raw and cooked potatoes cultivated in different geographical regions of Madeira Island (Portugal). Good results were obtained in terms of figures of the merit of the method, with correlation coefficients (R2) higher than 0.999 and recoveries between 94.5% to 125%. Method limit of detection (LOD) and limit of quantification (LOQ) were 0.14 μg/Kg and 0.43 μg/Kg, respectively, which are much lower than the accepted and legislated requirements by the European Union, which is 20 μg/Kg for chlorpropham. The concentration of chlorpropham in raw potatoes is significantly higher when compared to cooked samples, which revealed that the thermic treatment during cooking had a significant effect on its degradation. A significant decrease (90%, on average) was observed on chlorpropham levels.

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“…On the contrary, virus-based diseases are difficult to control due to the lack of anti-viral treatment where vector interference is the most common strategy. Furthermore, there are many actions that can be performed to assist in the control of plant virus-based diseases such as early symptom detection and vector control [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of New RGB Vegetation Indices for PHYVV and TMV Identification in Jalapeño Pepper (Capsicum annuum) Leaves Using CNNs-Based Model

Yee-Rendón

Torres‐Pacheco

Trujillo-Lopez

et al. 2021

Plants

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Recently, deep-learning techniques have become the foundations for many breakthroughs in the automated identification of plant diseases. In the agricultural sector, many recent visual-computer approaches use deep-learning models. In this approach, a novel predictive analytics methodology to identify Tobacco Mosaic Virus (TMV) and Pepper Huasteco Yellow Vein Virus (PHYVV) visual symptoms on Jalapeño pepper (Capsicum annuum L.) leaves by using image-processing and deep-learning classification models is presented. The proposed image-processing approach is based on the utilization of Normalized Red-Blue Vegetation Index (NRBVI) and Normalized Green-Blue Vegetation Index (NGBVI) as new RGB-based vegetation indices, and its subsequent Jet pallet colored version NRBVI-Jet NGBVI-Jet as pre-processing algorithms. Furthermore, four standard pre-trained deep-learning architectures, Visual Geometry Group-16 (VGG-16), Xception, Inception v3, and MobileNet v2, were implemented for classification purposes. The objective of this methodology was to find the most accurate combination of vegetation index pre-processing algorithms and pre-trained deep- learning classification models. Transfer learning was applied to fine tune the pre-trained deep- learning models and data augmentation was also applied to prevent the models from overfitting. The performance of the models was evaluated using Top-1 accuracy, precision, recall, and F1-score using test data. The results showed that the best model was an Xception-based model that uses the NGBVI dataset. This model reached an average Top-1 test accuracy of 98.3%. A complete analysis of the different vegetation index representations using models based on deep-learning architectures is presented along with the study of the learning curves of these deep-learning models during the training phase.

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A knowledge-based approach to designing control strategies for agricultural pests

Cited by 8 publications

References 40 publications

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

µQuEChERS Combined with UHPLC-PDA as a State-of-the-Art Analytical Approach for Quantification of Chlorpropham in Potato

Analysis of New RGB Vegetation Indices for PHYVV and TMV Identification in Jalapeño Pepper (Capsicum annuum) Leaves Using CNNs-Based Model

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