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Journal articles on the topic 'Classes of pests'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Makhmudova, Shahnoza, and Khonoyim Ergasheva. "Main pests of mung bean and the effectiveness of chemicals use in pest management." E3S Web of Conferences 284 (2021): 03020. http://dx.doi.org/10.1051/e3sconf/202128403020.

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In this paper, our studies have shown that a total of 27 species of pests belonging to 7 classes of 2 genus occur and cause damage in replanted mung bean agrobiocenosis. The highest biological efficiency in the cultivation of alfalfa in Mung bean is Entolucho 20% - 0.3 l/ha, Bagira 20% -0.3 l/ha, Karache Duo 25% - 0.3 l/ha. When using chemicals in the amount of BI-58 (new) 40% - 1.0 l/ha, Imidogold 35% - 0.25 l, Lamdex SC 5% - 0.5 l/ha against bruxus, more than 85-90% biological efficiency was achieved in our research.

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Resti, Yulia, Desi Herlina Saraswati, Des Alwine Zayanti, and Ning Eliyati. "CLASSIFICATION OF DISEASES AND PESTS OF MAIZE USING MULTINOMIAL LOGISTIC REGRESSION BASED ON RESAMPLING TECHNIQUE OF K-FOLD CROSS-VALIDATION." Indonesian Journal of Engineering and Science 3, no. 3 (2022): 069–76. http://dx.doi.org/10.51630/ijes.v3i3.83.

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Some of the obstacles in the cultivation of maize that cause low productivity of maize yields are diseases and pests. Early detection of maize diseases and pests is expected to reduce farmer losses. A system for the early detection of diseases and pests can be created by classifying them based on digital images. This study aimed to classify maize diseases and pests using multinomial logistic regression. The model and testing resampling were based on resampling technique of k-fold cross-validation. The research data was obtained from the RGB color feature extraction process for each object in e

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Toyoda, Hideyoshi. "Electrostatic Techniques for Physically Managing Pathogens, Insect Pests, and Weeds in Field and Greenhouse Cropping Systems." Agronomy 13, no. 12 (2023): 2855. http://dx.doi.org/10.3390/agronomy13122855.

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Dimas Saputra, Archamul Fajar Pratama, Muhammad Dawam Fakhri, Muhammad Ahsanur Rafi, and Fetty Tri Anggraeny. "CLASSIFICATION OF INSECT PESTS IN AGRICULTURE USING INCEPTION-RESNET-V2 ARCHITECTURE." Antivirus : Jurnal Ilmiah Teknik Informatika 19, no. 1 (2025): 41–51. https://doi.org/10.35457/antivirus.v19i1.4107.

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Object recognition in images is a major challenge in digital image processing with wide applications, including agriculture. This research aims to develop a Convolutional Neural Network (CNN) model based on the Inception-ResNet-V2 architecture for insect pest classification in agriculture. The dataset contains 1,591 images from 13 pest classes, which were processed through preprocessing stages such as resizing, normalization, and augmentation to enhance data quality and variation. The model training process was conducted for 10 epochs, resulting in an accuracy of 89.52% with a loss of 0.4024.

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Visutsak, Porawat. "Ontology-Based Semantic Retrieval for Durian Pests and Diseases Control System." International Journal of Machine Learning and Computing 11, no. 1 (2021): 92–97. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1019.

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In Southeast Asia, durian is affectionately called the king of fruit. Durian is the most popular crop planted in eastern and southern of Thailand. The total crop is around 600,000 tons per year; among this, 500,000 tons of the total production were exported worldwide. In Thailand, the knowledge of durian production is based on experience from generation to generation, especially the knowledge of durian pests and diseases control. This paper presents the ontology knowledge based for durian pests and diseases retrieval system. The major contributions of the system consist of 1) the stored knowle

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Bondarenko, I. V., N. P. Sekun, and O. G. Vlasova. "The pests of grain of spiked cultures during storage." Interdepartmental Thematic Scientific Collection of Plant Protection and Quarantine, no. 62 (September 3, 2016): 64–71. http://dx.doi.org/10.36495/1606-9773.2016.62.64-71.

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The results of monitoring of species composition and relative quantity insects and mites — pests and polluters of grain of spiked cultures during storage were presented. Detected 80 species of insects and mites, which belong to two classes, 7 orders and 30 families. The dominant species among them were identified. It was agreed, that level of occupancy and contamination of grain related to the temperature and moisture of environment, grain and stored culture.

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Siahaan, Parluhutan, Saroyo Saroyo, Agustina Monalisa Tangapo, and Susan Marlein Mambu. "Diversity of Pests and Natural Enemies in Rice Fields in Kiniar Village, East Tondano District, North Sulawesi-Indonesia." Journal of Applied Agricultural Science and Technology 8, no. 2 (2024): 200–210. http://dx.doi.org/10.55043/jaast.v8i2.226.

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A study was conducted in Kiniar Village, East Tondano District, North Sulawesi, to investigate the variety of pests and their natural adversaries in rice fields. The primary objective of this research was to ascertain the count of insect that act as pests, the number of natural enemy, diversity index, dominance index, and species relative abundance of insect pests and natural enemies in rice fields in Kiniar Village. The results showed that there are 40 insects and spider from 29 families and eight orders in local rice cultivation in Kiniar Village. These species were further categorized into

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8

Haider, Zeeshan Ali, Fida Muhammad Khan, Inam Ullah Khan, Muhammad Ali Khan, and Rahim Khan. "Early Detection and Prediction of Pests in Field Crops Using Transfer Learning." VFAST Transactions on Software Engineering 12, no. 3 (2024): 98–113. http://dx.doi.org/10.21015/vtse.v12i3.1874.

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This research study addresses the problem of early detection and prediction of pests in field crops. The primary objective of this research is to identify and distinguish pest species from an open-source dataset that includes 5,494 images across 12 classes. We developed an efficient model with a high probability of detecting pests in field crops using pre-trained models such as EfficientNetV2 and deep learning techniques. We applied hyperparameter tuning to the model to enhance its accuracy. Our proposed model is designed to detect and predict pests at an early stage, thereby preventing crop d

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Gomes, Jacó C., and Díbio L. Borges. "Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages Classification." Agronomy 12, no. 8 (2022): 1733. http://dx.doi.org/10.3390/agronomy12081733.

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Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficult due to the high similarity between species at similar maturity stages. This research proposes a solution to this problem using a few-shot learning approach. First, a novel insect data set based on curated images from IP102 is presented. The IP-FSL data set is composed of 97 classes of adult insect images, and 45 classes of early stages, totallin

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Lima, Alessandra R., Lucas D. Dias, Matheus Garbuio, Natalia M. Inada, and Vanderlei S. Bagnato. "A look at photodynamic inactivation as a tool for pests and vector-borne diseases control." Laser Physics Letters 19, no. 2 (2022): 025601. http://dx.doi.org/10.1088/1612-202x/ac4591.

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Abstract The control of pests and vector-borne diseases (VDBs) are considered public health issues Worldwide. Among the control techniques and pesticides used so far, photodynamic inactivation (PDI) has been shown as an eco-friendly, low cost, and efficient approach to eliminate pests and VDBs. PDI is characterized using a photosensitizing molecule, light and molecular oxygen (O2) resulting in production of reactive oxidative species which can promote the oxidation of biomolecules on pests and vectors. Herein, we review the past 51 years (1970–2021) regarding the use of photo pesticides, repor

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Kang, Chenrui, Lin Jiao, Rujing Wang, Zhigui Liu, Jianming Du, and Haiying Hu. "Attention-Based Multiscale Feature Pyramid Network for Corn Pest Detection under Wild Environment." Insects 13, no. 11 (2022): 978. http://dx.doi.org/10.3390/insects13110978.

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A serious outbreak of agricultural pests results in a great loss of corn production. Therefore, accurate and robust corn pest detection is important during the early warning, which can achieve the prevention of the damage caused by corn pests. To obtain an accurate detection of corn pests, a new method based on a convolutional neural network is introduced in this paper. Firstly, a large-scale corn pest dataset has been constructed which includes 7741 corn pest images with 10 classes. Secondly, a deep residual network with deformable convolution has been introduced to obtain the features of the

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M., Azath, Melese Zekiwos, and Abey Bruck. "Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis." Journal of Electrical and Computer Engineering 2021 (June 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/9981437.

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Cotton is one of the economically significant agricultural products in Ethiopia, but it is exposed to different constraints in the leaf area. Mostly, these constraints are identified as diseases and pests that are hard to detect with bare eyes. This study focused to develop a model to boost the detection of cotton leaf disease and pests using the deep learning technique, CNN. To do so, the researchers have used common cotton leaf disease and pests such as bacterial blight, spider mite, and leaf miner. K-fold cross-validation strategy was worn to dataset splitting and boosted generalization of

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13

Deghiche-Diab, Nacima, and Tesnim Deghiche. "New records and check list of arthropods from two oasis ecosystems in Algeria." Studia Universitatis Babeş-Bolyai Biologia 67, no. 1 (2022): 89–105. http://dx.doi.org/10.24193/subbbiol.2022.1.05.

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An arthropod sampling survey was performed in two palm groves from the wilaya of Biskra and Ouled Djellal. During 12 months (2020) of survey, the obtained results indicated the presence of 117 taxons divided into 2 classes (Insecta =103 species and Arachnids = 11 species). The species belonged to the orders: Coleoptera, Diptera, Orthoptera, and Hymenoptera. The Coleoptera order was the most represented in the two palm groves (32 from Ouled Djellal (OD) and 6 from Feliache (Fe)). The major trophic guild represented in the oasis ecosystems was the predator guild (OD= 39%, Fe=32%) in comparison t

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D. R, Mrs Dr Thirupurasundari. "Agriculture Pest Classification using Deep CNN Model." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 307–17. https://doi.org/10.22214/ijraset.2025.68207.

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Agricultural pests are spurs of economic, social and sorrowful environmental impacts around the globe. To control these pests proper identification and categorization is prudent in strategies used to tackle them. This work highlights the DeepPestNet, a CNN built specifically for accurately identifying nine classes of pests important in agriculture. Base onto the transfer learning of EfficientNetB0 which was developed to boost the performance of pest recognition, DeepPestNet has more convolutional and attention layers incorporated into the framework. Training and evaluation on this broad set sh

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15

Toukem, Nadia K., Abdullahi A. Yusuf, Thomas Dubois, Elfatih M. Abdel-Rahman, Marian Salim Adan, and Samira A. Mohamed. "Landscape Vegetation Productivity Influences Population Dynamics of Key Pests in Small Avocado Farms in Kenya." Insects 11, no. 7 (2020): 424. http://dx.doi.org/10.3390/insects11070424.

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Avocado (Persea americana Mill.) production contributes to the economic growth of East Africa. However, poor fruit quality caused by infestations of tephritid fruit flies (Tephritidae) and the false codling moth, Thaumatotibia leucotreta (Meyrick), hampers access to lucrative export markets. Remote sensing and spatial analysis are increasingly applied to crop pest studies to develop sustainable and cost-effective control strategies. In this study, we assessed pest abundance in Muranga, Kenya, across three vegetation productivity classes, viz., low, medium and high, which were estimated using t

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16

Bezliudnyi, Y. S., V. M. Shymkovysh, and A. Yu Doroshenko. "Convolutional neural network model and software for classification of typical pests." PROBLEMS IN PROGRAMMING, no. 4 (December 2021): 095–102. http://dx.doi.org/10.15407/pp2021.04.095.

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A model of a convolutional neural network, a dataset for neural network training, and a software tool for the classification of typical insect pests have been developed, which allows recognizing the class of insect pests from an image. The structure of the neural network model was optimized to improve the classification results. In addition, the user interface, authentication, and authorization, data personalization, the presence of user roles and the appropriate distribution of functionality by role, the ability to view statistics on classified insects in a certain period of time were develop

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17

Longo, Antonello, Maria Rizzi, and Cataldo Guaragnella. "Improving Classification Performance by Addressing Dataset Imbalance: A Case Study for Pest Management." Applied Sciences 15, no. 10 (2025): 5385. https://doi.org/10.3390/app15105385.

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Imbalanced data are a non-trivial problem in deep learning. The high variability in the number of samples composing each category might force learning procedures to become biased towards classes with major cardinality and disregard classes with low instances. To overcome such limitations, common strategies involve data balancing using resampling techniques. The cardinality of overnumbered categories is often lowered by sample deletion, thus reducing the data space where the model can learn from. This paper introduces a new approach based on data balancing without sample deletion, allowing for

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18

Prokopy, Ronald J., Daniel R. Cooley, Wesley R. Autio, and William M. Coli. "Second-level integrated pest management in commercial apple orchards." American Journal of Alternative Agriculture 9, no. 4 (1994): 148–56. http://dx.doi.org/10.1017/s0889189300005890.

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AbstractAs historical background helpful to understanding current concepts and practices of apple pest management, we review the origin and rise of key pests of apple in North America and the evolution of approaches to their management, culminating with the concept of integrated pest management (IPM). We propose four levels of integration of orchard pest management practices. First-level IPM integrates chemically based and biologically based management tactics for a single class of pests, such as arthropods, diseases, weeds or vertebrates. Second-level IPM, the focus of our effort here, integr

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19

Karim, Iim Abdul. "Dinamika Model Predator Hama Wereng Batang Cokelat (Nilaparvata Lugens) Pada Tanaman Padi Dengan Penerapan Pestisida." Faktor Exacta 11, no. 1 (2018): 94. http://dx.doi.org/10.30998/faktorexacta.v11i1.2412.

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<p><span><span lang="IN"><span style="font-size: medium;">Abstract. In this paper, the mathematical model we discuss the interactions among pests, predators, and effect of pesticides. Interactions between predators and pests use functional responses of Holling type I and type II and the growth of susceptible pests classes satisfied the logistic function. By this model, the existence and stability of the equilibrium point were performed. The existence of the equilibrium point, and were obtained which depend on the threshold parameter, while the equilibrium point did not

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20

Borzykh, A., G. Tkalenko, I. Kirichuk, and A. Chelombitko. "The conducting pheromone monitoring of the main pests of the orchard." Interdepartmental Thematic Scientific Collection of Plant Protection and Quarantine, no. 66 (December 24, 2020): 3–16. http://dx.doi.org/10.36495/1606-9773.2020.66.3-16.

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Goal. Analysis of the latest methods for identifying pests of an orchard using pheromone monitoring and factors affecting its implementation. Methods. The studies were carried out in accordance with modern methodological approaches to pheromone monitoring of pests of fruit plantations. Results. The principles of pheromone monitoring in an orchard are given to identify harmful and quarantine organisms, which makes it possible to obtain information about the presence of pests in a certain area, determine their number, development dynamics, and, on the basis of the data obtained, plan

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21

Öztürk, Övünç. "TACO: An Ontology in Turkish for Identifying and Controlling Plant Pests, Weeds and Diseases." Deu Muhendislik Fakultesi Fen ve Muhendislik 26, no. 77 (2024): 242–47. http://dx.doi.org/10.21205/deufmd.2024267707.

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While ensuring a more sustainable production, because of reduced chemical usage it is more complicated to control plant pests, diseases and weeds in smart agriculture. For this reason, it is of great importance to detect pests, diseases and weeds at the earliest stage. It is important that both farmers and the artificial intelligence applications developed for agricultural control should be able to detect these organisms and to know the agricultural control methods. Semantic technologies and ontologies provide machine interpretable information and solutions for heterogeneity. This study presen

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Resti, Yulia, Chandra Irsan, Mega Tiara Putri, Irsyadi Yani, Ansyori Ansyori, and Bambang Suprihatin. "Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor." Science and Technology Indonesia 7, no. 1 (2022): 29–35. http://dx.doi.org/10.26554/sti.2022.7.1.29-35.

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Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the qu

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23

Yang, Shuai, Ziyao Xing, Hengbin Wang, et al. "Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection." Insects 14, no. 3 (2023): 278. http://dx.doi.org/10.3390/insects14030278.

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The frequent occurrence of crop pests and diseases is one of the important factors leading to the reduction of crop quality and yield. Since pests are characterized by high similarity and fast movement, this poses a challenge for artificial intelligence techniques to identify pests in a timely and accurate manner. Therefore, we propose a new high-precision and real-time method for maize pest detection, Maize-YOLO. The network is based on YOLOv7 with the insertion of the CSPResNeXt-50 module and VoVGSCSP module. It can improve network detection accuracy and detection speed while reducing the co

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Vasyliev, S. V. "Efficiency of insecticides against the sucking phyllophages in apple orchards on drip irrigation in the Eastern Forest-Steppe of Ukraine." Kharkov Entomological Society Gazette 29, no. 2 (2021): 40–49. http://dx.doi.org/10.36016/khesg-2021-29-2-4.

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The effect of insecticides on the main pests of apple leaves has been studied. Nine species of phyllophagous insects have been identified. The green apple aphid (Aphis pomі (De Geer, 1773)) and the apple leaf midge (Dasineura mali (Kieffer, 1904)) were of economic importance. Insecticides of different chemical classes (neonicotinoids and ketoenols) were used against the pests. The studied chemical preparations had a high protective effect against the aphids and the gall midge larvae. Movento 100SC, CS (2.0 l/ha) was the most effective insecticide, it had a technical efficiency ranges from 92.6

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Fajri, Diny Melsye Nurul, Wayan Firdaus Mahmudy, and Titiek Yulianti. "Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network." Journal of Information Technology and Computer Science 6, no. 1 (2021): 18–24. http://dx.doi.org/10.25126/jitecs.202161195.

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Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting l

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Bianome, Restanti M., Yelly Y. Nabuasa, and Derwin R. Sina. "DIAGNOSA HAMA DAN PENYAKIT PADA TANAMAN PADI MENGGUNAKAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR." Jurnal Komputer dan Informatika 8, no. 2 (2020): 156–62. http://dx.doi.org/10.35508/jicon.v8i2.2906.

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This study builds systems Case Based Reasoning (CBR) to diagnose pests and diseases in rice plants using Naïve Bayes algorithm and K-Nearest Neighbor. CBR is one method of solving the problem with new cases of decision making based on the solution of previous cases by calculating the degree of similarity (similarity), The case consists of 13 species and 10 types of disease pests of rice plants. The degree of similarity can be determined by indexing and nonindexing. Indexing is the process of grouping the cases by classes that have been determined, while nonindexing a process without grouping c

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Lazko, V. E., E. N. Blagorodova, O. V. Yakimova, and E. V. Kovaleva. "Experience of application of bioinsectoacaricide MatrinBio in film greenhouse on gourds." Vegetable crops of Russia, no. 2 (April 8, 2023): 65–69. http://dx.doi.org/10.18619/2072-9146-2023-2-65-69.

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Relevance. The study is aimed at evaluating the biological effectiveness of insecticides to reduce the number of tobacco thrips and gourd aphids in a film greenhouse on gourds. These pests cause significant damage to plants and can carry a viral infection. In greenhouse conditions, tobacco thrips can produce up to 7-8 generations, and melon aphid – up to 16 generations per season. The recommended pest control products have a limited protective duration in protected ground conditions, and in most cases promote the development of resistance in insects.Results. The article presents the results on

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Alpyssov, Akan, Nurgul Uzakkyzy, Ayazbaev Talgatbek, et al. "Assessment of plant disease detection by deep learning." Eastern-European Journal of Enterprise Technologies 1, no. 2 (121) (2023): 41–48. http://dx.doi.org/10.15587/1729-4061.2023.274483.

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Plant disease and pest detection machines were originally used in agriculture and have, to some extent, replaced traditional visual identification. Plant diseases and pests are important determinants of plant productivity and quality. Plant diseases and pests can be identified using digital image processing. According to the difference in the structure of the network, this study presents research on the detection of plant diseases and pests based on three aspects of the classification network, detection network, and segmentation network in recent years, and summarizes the advantages and disadv

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Akan, Alpyssov, Uzakkyzy Nurgul, Talgatbek Ayazbaev, et al. "Assessment of plant disease detection by deep learning." Eastern-European Journal of Enterprise Technologies 1, no. 2 (121) (2023): 41–48. https://doi.org/10.15587/1729-4061.2023.274483.

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Plant disease and pest detection machines were originally used in agriculture and have, to some extent, replaced traditional visual identification. Plant diseases and pests are important determinants of plant productivity and quality. Plant diseases and pests can be identified using digital image processing. According to the difference in the structure of the network, this study presents research on the detection of plant diseases and pests based on three aspects of the classification network, detection network, and segmentation network in recent years, and summarizes the advantages and disadv

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Agustian, Indra, Ruvita Faurina, Sahrial Ihsani Ishak, Ferzha Putra Utama, Kusmea Dinata Dinata, and Novalio Daratha. "Deep learning pest detection on Indonesian red chili pepper plant based on fine-tuned YOLOv5." International Journal of Advances in Intelligent Informatics 9, no. 3 (2023): 383. http://dx.doi.org/10.26555/ijain.v9i3.864.

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.This research developed a pest detection model for Indonesian red chili pepper based on fine-tuned YOLOv5. Indonesian red chili pepper is the third largest vegetable commodity produced in Indonesia. Pest attacks disrupt the quantity and quality of crop yields. To control pests effectively, it is necessary to detect the type of pest correctly. A viable solution is to leverage computer vision and deep learning technologies. However, no previous studies have developed a pest detection model for Indonesian red chili pepper based on this technology. YOLOv5 is a variant of the YOLO object detection

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Zhu, Jiang, Ruipeng Chen, Juan Liu, et al. "Presence of Multiple Genetic Mutations Related to Insecticide Resistance in Chinese Field Samples of Two Phthorimaea Pest Species." Insects 15, no. 3 (2024): 194. http://dx.doi.org/10.3390/insects15030194.

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Potatoes hold the distinction of being the largest non-cereal food crop globally. The application of insecticides has been the most common technology for pest control. The repeated use of synthetic insecticides of the same chemical class and frequent applications have resulted in the emergence of insecticide resistance. Two closely related pests that feed on potato crops are the potato tuber moth, Phthorimaea operculella, and the tomato leafminer, Phthorimaea absoluta (syn. Tuta absoluta). Previous studies indicated the existence of insecticide resistance to various classes of insecticides inc

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Grantina, Ingrida, and Inara Turka. "The Influence of Synthetic Insecticides on the Dynamics of Cabbage Stem Weevil (Ceutorhynchus pallidactylus Marsh.) and Cabbage Pod Weevil (Ceutorhynchus obstrictus Marsh.) in Winter Oilseed Rape." Proceedings of the Latvia University of Agriculture 28, no. 1 (2013): 60–68. http://dx.doi.org/10.2478/v10236-012-0016-z.

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Abstract Oilseed rape is affected by a complex of different crucifer pests. Cruciferous stem weevils (Ceuthorhynchus spp.) are relatively new oilseed rape (Brassica napus L.) pests in Latvia. Although currently brassica pest control is performed according to the appearance of first specimens of Ceutorhynchus or other brassica pests, for a large number of insecticide treatments a positive biological efficiency is observed (however, within a large range – 8–98%). The current application of brassica pest control may be described as preventive, which is not permitted in the integrated pest managem

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Liu, Dayang, Feng Lv, Jingtao Guo, Huiting Zhang, and Liangkuan Zhu. "Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning." Forests 14, no. 7 (2023): 1484. http://dx.doi.org/10.3390/f14071484.

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Infestations or parasitism by forestry pests can lead to adverse consequences for tree growth, development, and overall tree quality, ultimately resulting in ecological degradation. The identification and localization of forestry pests are of utmost importance for effective pest control within forest ecosystems. To tackle the challenges posed by variations in pest poses and similarities between different classes, this study introduced a novel end-to-end pest detection algorithm that leverages deep convolutional neural networks (CNNs) and a transfer learning technique. The basic architecture of

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Shyta, О. "The potato protection from major pests and diseases." Karantin i zahist roslin, no. 1-2 (January 20, 2019): 18–21. http://dx.doi.org/10.36495/2312-0614.2019.1-2.18-21.

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Goal. To study the effectiveness of pesticides in protecting potato plantations from major pests and diseases. Methods. Comparative, analytical and field. Results. The data of technical and economic efficiency of pesticides against the main pests and diseases of potatoes are given. It was noted that the most effective against the complex of pests were drugs from the group of neocotinoids, and against diseases — fungicides of systemic and systemic contact action of various classes of chemical compounds. Conclusions. The most common potato diseases during the growing season of

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Marini, Lorenzo, Matthew P. Ayres, and Hervé Jactel. "Impact of Stand and Landscape Management on Forest Pest Damage." Annual Review of Entomology 67, no. 1 (2022): 181–99. http://dx.doi.org/10.1146/annurev-ento-062321-065511.

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One promising approach to mitigate the negative impacts of insect pests in forests is to adapt forestry practices to create ecosystems that are more resistant and resilient to biotic disturbances. At the stand scale, local stand management practices often cause idiosyncratic effects on forest pests depending on the environmental context and the focal pest species. However, increasing tree diversity appears to be a general strategy for reducing pest damage across several forest types. At the landscape scale, increasing forest heterogeneity (e.g., intermixing different forest types and/or age cl

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Wu, Xiaojiang, Jinzhe Liang, Yiyu Yang, et al. "SAW-YOLO: A Multi-Scale YOLO for Small Target Citrus Pests Detection." Agronomy 14, no. 7 (2024): 1571. http://dx.doi.org/10.3390/agronomy14071571.

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Citrus pests pose a major threat to both citrus yield and fruit quality. The early prevention of pests is essential for sustainable citrus cultivation, cost savings, and the reduction of environmental pollution. Despite the increasing application of deep learning techniques in agriculture, the performance of existing models for small target detection of citrus pests is limited, mainly in terms of information bottlenecks that occur during the transfer of information. This hinders its effectiveness in fully automating the detection of citrus pests. In this study, a new approach was introduced to

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Babayan, A. H. "Pest species composition of fragrant trees in the parks and gardens of Yerevan City, Armenia." SABRAO Journal of Breeding and Genetics 54, no. 1 (2022): 201–9. http://dx.doi.org/10.54910/sabrao2022.54.1.19.

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Several fragrant tree species, i.e., Aesculus hippocastanum, Magnolia brooklynensis „Yellow Bird,‟ Catalpa bignonioides, and Prunus serrulata were previously registered in the database. A. hippocastanum, Ailanthus altíssima, C. bignonioides, and Robinia pseudoacacia were found to be the most common and important species in the parks and gardens of Yerevan, Armenia. These species were found in almost all of the studied gardens. The present study aimed to investigate the species composition of the pests of fragrant trees in different landscaped areas of Yerevan, Armenia, during 2020–2021. This s

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Khudoykulov, Azamjon, Azimjon Anorbaev, Malika Norova, and Jabbor Abdiev. "Development characteristics of underground pests of root vegetable and potato crops." E3S Web of Conferences 421 (2023): 02015. http://dx.doi.org/10.1051/e3sconf/202342102015.

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The present article delves into a comprehensive examination of the species composition of pests prevalent within fields cultivating root vegetables and potatoes. Through systematic scrutiny, the study reveals the presence of pest representatives spanning three distinct classes, six genera, eleven families, and twenty-nine species within a potato field subject to repeated cultivation. Among these discerned species, a detailed analysis is focused on sixteen varieties of subterranean pests originating from four distinct genera. Concurrently, the research encompasses a decade-long assessment of th

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Resti, Yulia, Chandra Irsan, Adinda Neardiaty, Choirunnisa Annabila, and Irsyadi Yani. "Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests." Mathematics 11, no. 8 (2023): 1761. http://dx.doi.org/10.3390/math11081761.

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As an agricultural commodity, corn functions as food, animal feed, and industrial raw material. Therefore, diseases and pests pose a major challenge to the production of corn plants. Modeling the classification of corn plant diseases and pests based on digital images is essential for developing an information technology-based early detection system. This plant’s early detection technology is beneficial for lowering farmers’ losses. The detection system based on digital images is also cost-effective. This paper aims to model the classification of corn plant diseases and pests based on digital i

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Halliday, W. R., N. O. Morgan, and R. L. Kirkpatrick. "EVALUATION OF INSECTICIDES FOR CONTROL OF STORED-PRODUCT PESTS IN TRANSPORT VEHICLES1." Journal of Entomological Science 22, no. 3 (1987): 224–36. http://dx.doi.org/10.18474/0749-8004-22.3.224.

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Thirty-three insecticides were evaluated over a 6 year period for their effectiveness against three species of stored-product insects: the confused flour beetle, Tribolium confusum Jacquelin duVal; the black carpet beetle, Attagenus unicolor (Brahm); and a warehouse beetle, Trogoderma glabrum (Herbst). The tests were conducted in transport trailer vans or sea-going cargo containers. The insecticides were formulated for application as aerosols or dusts or both. Dusts generally caused greater mortality than aerosols. Pyrethroids were more effective than other classes of insecticides tested. Cyfl

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Savinelli, Caydee, John Immaraju, Chuck Schiller, et al. "Perspectives from the Crop Protection Industry: Suggestions for Collaborative Resistance Management." Plant Health Progress 8, no. 1 (2007): 62. http://dx.doi.org/10.1094/php-2007-0719-07-ps.

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IRAC-US is a specialist technical group of CropLife America. It provides a coordinated crop protection industry response to prevent or delay the development of resistance in insect and mite pests. Its aim is to keep all classes of insecticides and acaricides as viable control options. The neonicotinoid subcommittee of IRAC, U.S. has developed resistance management guidelines for the use of the neonicotinoid class of chemistry. For additional information about IRAC, US visit the website: www.irac-online.org. Accepted for publication 6 July 2006. Published 19 July 2007.

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Avazov, S. "Onion pathogens during cultivation and storage." Bulletin of Science and Practice 4, no. 2 (2018): 183–85. https://doi.org/10.5281/zenodo.1173169.

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The results of the study of the composition of onion diseases and their pathogens. Studies to identify the composition of bows diseases were carried out on the fields of farmers of Tashkent region. From 2016 to 2018 all were found only 57 species from 29 genera, 11 families, 7 orders and 4 classes of fungi. 9 species were the most commonly occurs in the conditions of Tashkent region. The characteristic features of the spread and development of pests of onions in the field and during storage are revealed.

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Melnichuk, F. S., S. A. Alekseeva, and O. V. Hordiienko. "PROTECTION OF POTATO CROPS AGAIST PESTS." Міжвідомчий тематичний науковий збірник "Меліорація і водне господарство", no. 1 (July 22, 2019): 99–107. http://dx.doi.org/10.31073/mivg201901-166.

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The Colorado potato beetles and aphids are especially harmful pests for potato crops in the conditions of Kyiv region. So, on the 10th day after mass rebirth of the Colorado potato beetle larvae, potato plants on untreated by insecticide plots were completely destroyed by this phytophagus. Preplanting insecticide treatment of potato tubers provided high effectiveness against the Colorado potato beetle prior to the mass rebirth and development of larvae and reduced their density of population and harmfulness. The highest efficacy (93.2-95.2%) against these pests was noted in variants with Prest

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Li, Dawei, Foysal Ahmed, Nailong Wu, and Arlin I. Sethi. "YOLO-JD: A Deep Learning Network for Jute Diseases and Pests Detection from Images." Plants 11, no. 7 (2022): 937. http://dx.doi.org/10.3390/plants11070937.

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Recently, disease prevention in jute plants has become an urgent topic as a result of the growing demand for finer quality fiber. This research presents a deep learning network called YOLO-JD for detecting jute diseases from images. In the main architecture of YOLO-JD, we integrated three new modules such as Sand Clock Feature Extraction Module (SCFEM), Deep Sand Clock Feature Extraction Module (DSCFEM), and Spatial Pyramid Pooling Module (SPPM) to extract image features effectively. We also built a new large-scale image dataset for jute diseases and pests with ten classes. Compared with other

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Jia, Xinyu, Xueqin Jiang, Zhiyong Li, Jiong Mu, Yuchao Wang, and Yupeng Niu. "Application of Deep Learning in Image Recognition of Citrus Pests." Agriculture 13, no. 5 (2023): 1023. http://dx.doi.org/10.3390/agriculture13051023.

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The occurrence of pests at high frequencies has been identified as a major cause of reduced citrus yields, and early detection and prevention are of great significance to pest control. At present, studies related to citrus pest identification using deep learning suffer from unbalanced sample sizes between data set classes, which may cause slow convergence of network models and low identification accuracy. To address the above problems, this study built a dataset including 5182 pest images in 14 categories. Firstly, we expanded the dataset to 21,000 images by using the Attentive Recurrent Gener

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Lumbantoruan, Rosni, Nico Rajagukguk, Anju Ucok Lubis, Marwani Claudia, and Humasak Simanjuntak. "Two-step convolutional neural network classification of plant disease." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 584. http://dx.doi.org/10.11591/ijai.v14.i1.pp584-591.

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Indonesia is primarily an agricultural country, with farming being the primary source of income for most of its people. Unfortunately, crop production is vulnerable to plant diseases, which are usually caused by plant pests, resulting in a reduction in both the quantity and quality of the expected harvest. In addition to the large number of classes to predict, detecting and accurately classifying each disease on different plants can be difficult. We believe that limiting the number of classes to identify may improve classification accuracy. Thus, in this research, we propose a new approach, tw

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Rosni, Lumbantoruan, Rajagukguk Nico, Ucok Lubis Anju, Claudia Marwani, and Simanjuntak Humasak. "Two-step convolutional neural network classification of plant disease." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 584–91. https://doi.org/10.11591/ijai.v14.i1.pp584-591.

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Indonesia is primarily an agricultural country, with farming being the primary source of income for most of its people. Unfortunately, crop production is vulnerable to plant diseases, which are usually caused by plant pests, resulting in a reduction in both the quantity and quality of the expected harvest. In addition to the large number of classes to predict, detecting and accurately classifying each disease on different plants can be difficult. We believe that limiting the number of classes to identify may improve classification accuracy. Thus, in this research, we propose a new approach, tw

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Hallimah Rizqi Putti, Linda Tri Andini, Oktavia Nur Qhoirunnisa, Rr. Shafira Indranovianti, and Dhian Satria Yudha Kartika. "Penerapan Teknologi Tepat Guna Dalam Pengembangan Budidaya Tanaman Menggunakan Metode Hidroponik Di SDN Wonosalam 1." Jurnal Masyarakat Mengabdi Nusantara 2, no. 2 (2023): 64–72. https://doi.org/10.58374/jmmn.v2i2.150.

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Hydroponics is a method of cultivating plants that does not use soil media but emphasizes meeting the nutritional needs of plants. Hydroponic cultivation requires less water than soil cultivation. Maintenance of hydroponic cultivation is very easy and can be done at any time regardless of the season. Cultivation of plants planted namely pakcoy. One of the advantages of growing pakcoy plants hydroponically is that they are very resistant to pests and diseases, so there is no need to control pests and diseases. The development of pakcoy hydroponic cultivation is used as an appropriate technology

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Rofidah, Erna, Siti Arofah, and Indah Trisnawati Dwi Tjahjaningrum. "THE EFFECT OF HABITAT MODIFICATION ON PADDY VARIETY IR 64 FIELD WITH TRAP CROP APPLICATION USING LEMON GRASS (Andropogon nardus ) AND WITHOUT TRAP CROP APPLICATION TOWARDS THE COMPOTITION, ABUNDANCE, AND DIVERSITY OF ARTHROPODS." KnE Life Sciences 2, no. 1 (2015): 605. http://dx.doi.org/10.18502/kls.v2i1.226.

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<p>The trap crop technique relies on the attraction of insect pests to plantings other than the main crop. Application using of lemon grass for Habitat modification on paddy field can alter species composition and community structure including Arthropods from insect groups. The lemon grass was planted 20 day before main crop (paddy variety IR 64). This study was conducted in Pasuruan, East Java. Samples were taken using sweep net on vegetative paddy phase, generative paddy phase and ripening paddy phase. Sampling periods from Desember 2012 to March 2013. Each sample was sorted and identi

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Robusti, Eliane Araújo, Humberto Godoy Androcioli, Fernando Teruhiko Hata, Dimas Soares Júnior, and Ayres Oliveira Menezes Júnior. "Farmers' and rural extension workers' perceptions on the adoption of the integrated pest management." DELOS: DESARROLLO LOCAL SOSTENIBLE 16, no. 46 (2023): 2294–307. http://dx.doi.org/10.55905/rdelosv16.n46-018.

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As one important element of food culture, the common bean can be found in diverse countries and social classes. Integrated Pest Management (IPM) can help rationalize the use of insecticides to control insect pests in bean crops. This grain is an important element for food security and can be found in different countries and social classes. The present study aimed to examine rural extension professionals' and farmers' after participating in the “Plante seu Futuro” (“Grow your future”) program of the Government of the State of Paraná-Brazil in its component “Integrated Management of Pests and Di

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