What Can We Learn from Dissecting Tortricid Females About the Efficacy of Mating Disruption Programs?

Insects, Год журнала: 2025, Номер 16(3), С. 248 - 248

Опубликована: Фев. 28, 2025

Female mating success for the tortricids codling moth (CM), Cydia pomonella, Oriental fruit (OFM), Grapholita molesta, European grape vine (EGVM), Lobesia botrana, and five leafroller (LR) species under various disruption (MD) programs was reviewed at a time when new dual sex lures can provide alternative tools to assess female mating. Previous reliance on passive assessments such as tethering virgin female-baited traps with laboratory moths are odds active trapping methods of wild moths. Additive factors delayed mating, adjustments in behaviors, greater levels natural control may or not contribute apparent MD. Current MD based solely research, economics commercialization require some compromise. The complete pheromone blend is always used. A delay has been reported from field one study suggested that reductions fecundity would likely be minimal. There no evidence works better low population densities. an established technology, but showing density mated females rather high. Efforts improve efficacy ongoing small cadre researchers.

Язык: Английский

---

Automatic Pest Monitoring Systems in Apple Production under Changing Climatic Conditions

Horticulturae, Год журнала: 2022, Номер 8(6), С. 520 - 520

Опубликована: Июнь 14, 2022

Apple is one of the most important economic fruit crops in world. Despite all strategies integrated pest management (IPM), insecticides are still frequently used its cultivation. In addition, phenology extremely influenced by changing climatic conditions. The frequent spread invasive species, unexpected outbreaks, and development additional generations some problems posed climate change. adopted IPM therefore need to be changed as do current monitoring techniques, which increasingly unreliable outdated. for more sophisticated, accurate, efficient techniques leading increasing automated systems. this paper, we summarize automatic methods (image analysis systems, smart traps, sensors, decision support etc.) monitor major apple production (Cydia pomonella L.) other pests (Leucoptera maifoliella Costa, Grapholita molesta Busck, Halyomorpha halys Stål, flies—Tephritidae Drosophilidae) improve sustainable under

Язык: Английский

---

An efficient mobile model for insect image classification in the field pest management

Engineering Science and Technology an International Journal, Год журнала: 2023, Номер 39, С. 101335 - 101335

Опубликована: Янв. 28, 2023

Accurately recognizing insect pest in their larva phase is significant to take the early treatment on infected crops, thus helping timely reduce yield loss agricultural products. The convolutional neural networks (CNNs)-based classification methods have become most competitive address many technical challenges related image recognition field. Focusing accurate and small models carried mobile devices, this study proposed a novel method PCNet (Pest Classification Network) based lightweight CNNs embedded attention mechanism. was designed with EfficientNet V2 as backbone, coordinate mechanism (CA) incorporated architecture learn inter-channel information positional of input images. Moreover, combining feature maps output by inverted bottleneck (MBConv) average pooling develop fusion module, which implements between shallow layers deep features down-sampling procedures. In addition, stochastic, pipeline-based data augmentation approach adopted randomly enhance diversity avoid model overfitting. experimental results show that achieved accuracy 98.4 % self-built dataset consisting 30 classes larvae, outperforms three classic CNN (AlexNet, VGG16, ResNet101), four (ShuffleNet V2, MobileNet V3, V1 V2). To further verify robustness different datasets, also tested two other public datasets: IP102 miniImageNet. 73.7 dataset, outperforming 94.0 miniImageNet only lower than ResNet101 V3. number parameters 20.7 M, less those traditional models. satisfactory size makes it suitable for real-time field resource constrained devices. Our code will be available at https://github.com/pby521/PCNet/tree/master.

Язык: Английский

---

Link Quality Modeling for LoRa Networks in Orchards

Опубликована: Май 5, 2023

LoRa networks have been deployed in many orchards for environmental monitoring and crop management. An accurate propagation model is essential efficiently deploying a network orchards, e.g., determining gateway coverage sensor placement. Although some models studied networks, they are not suitable orchard environments, because do consider the shadowing effect on wireless caused by ground tree canopies. This paper presents FLog, signals environments. FLog leverages unique feature of i.e., all trees similar shapes planted regularly space. We develop 3D orchards. Once we location gateway, know mediums that signal traverse. Based this knowledge, generate First Fresnel Zone (FFZ) between sender receiver. The intrinsic path loss exponents (PLE) can be combined into classic Log-Normal Shadowing FFZ. Extensive experiments almond show reduces link quality estimation error 42.7% improves accuracy 70.3%, compared with widely-used model.

Язык: Английский

---

Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review

Journal of Semiconductors, Год журнала: 2023, Номер 44(2), С. 023104 - 023104

Опубликована: Фев. 1, 2023

Abstract The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security. Early detection critical importance make accurate predictions, optimize control strategies prevent losses. Recent technological advancements highlight opportunity revolutionize monitoring diseases. Biosensing methodologies offer potential solutions for real-time automated monitoring, which allow in early thus support sustainable protection. Herein, advanced biosensing technologies including image-based technologies, electronic noses, wearable sensing methods are presented. Besides, challenges future perspectives widespread adoption these discussed. Moreover, we believe it necessary integrate through interdisciplinary cooperation further exploration, may provide unlimited possibilities innovations applications agriculture monitoring.

Язык: Английский

---