Academic literature on the topic 'Guava rust'

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Journal articles on the topic "Guava rust"

1

Martins, Marlon Vagner Valentim, Silvado Felipe da Silveira, and Luiz Antônio Maffia. "Guava fruit loss caused by rust." Summa Phytopathologica 40, no. 2 (2014): 107–13. http://dx.doi.org/10.1590/0100-5405/1904.

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The aim of this paper was to estimate the loss caused by rust (Puccinia psidii) to 'Paluma' guava production in orchards located in Rio de Janeiro State. The disease intensity on the reproductive organs of plants was observed during two chemical control experiments carried out in 2003 and 2004. The loss was estimated based on simple linear regression and the production reduction (fruit number and weight ha-1) on the incidence of diseased buds and fruits. In the first experiment, no relationship was established between incidence of diseased buds and loss since there was a delay in spraying and the incidence of buds showing rust was high (mean of 47%) at the beginning of flowering. In the second experiment (2003-2004), spraying occurred at the beginning of the epidemics and there was a linear relationship between incidence of diseased buds and loss, justifying fungicide intervention at this stage. For the fruiting stage, a significant relationship was found between incidence of diseased fruits and loss in both experiments. In the absence of chemical control, rust reduced fruit production by around 90%.
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2

DATAR, S. V., R. C. DUBEY, and N. CHATTOPADHYAY. "Studies on some diseases of fruits and vegetable crops in relation to meteorological parameters." MAUSAM 51, no. 3 (2021): 269–74. http://dx.doi.org/10.54302/mausam.v51i3.1785.

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The role of different meteorological parameters controlling the incidences of some diseases on tomato, guava and fig grown at Pune and Padegaon in Maharashtra state were studied by graphical superimposition and correlation techniques. Peak infestation of rust on fig was observed in January at Padegaon whereas maximum infestation of fruit canker on guava and early blight on tomato were reported respectively from September to first week of October and September to November at Pune. Increase in maximum temperature was found to increase the infestation of both fruit canker and early blight. Decrease in afternoon relative humidity favoured the infestation of fruit canker and rust. Fall of minimum temperature and rise in bright hours of sunshine also aggravated the incidences of fruit canker and rust respectively.
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3

Triwidodo, Hermanu, Efi Toding Tondok, and Desy Nur Avifah. "Susceptibility of Two Varieties of Guava (Psidium guajava L.) to Pest and Disease Infection in the Sub-District of Tanah Sareal, Bogor." Jurnal Perlindungan Tanaman Indonesia 25, no. 2 (2021): 106. http://dx.doi.org/10.22146/jpti.49851.

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This survey was done in several guava fields around Bogor and found many unhealthy plants infested by various plant pests and diseases. This study aimed to analyze the effect of various guava cultivars on pest and disease intensity on guava in the Sub-District of Tanah Sareal, Bogor. Samples were collected from 20 fields that consisted of 15 samples of Getas Merah and 5 samples of Bangkok Putih cultivars to identify plant pests and diseases. Interviews were done with field owners to obtain information on cultivating practices. Pests found based on morphological identification included plant bugs (Hemiptera: Miridae), locusts (Orthoptera: Acrididae), bagworms (Lepidoptera: Psychidae), Geometrid caterpillars (Lepidoptera: Geometridae), Pyralid caterpillars (Lepidoptera: Pyralidae), scale insects (Hemiptera: Coccidae), planthoppers (Hemiptera: Flatidae), and mealybugs (Hemiptera: Pseudococcidae). Disease and pathogens identified included red rust disease (Cephaleuros sp.), anthracnose (Colletotrichum gloeosporioides), and fruit cancer (Pestalotia sp.). The occurrence of biting-chewing type pests was greater than piercing-sucking type insects, including 33.33% and 34.99%. Red rust disease was the dominant disease found on both guava cultivars consisting of 68.88% and 63.33% of the samples.
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4

Saifu Rohman, Rina Mahmudati, and Wiko Anang Ansoruloh. "Sistem Pakar Diagnosa Penyakit pada Tanaman Jambu Kristal Menggunakan Metode Dempster Shafer Berbasis Web." Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer 4, no. 1 (2024): 65–73. http://dx.doi.org/10.51903/juritek.v4i1.2892.

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The Crystal Guava plant can be attacked by several types of diseases. Diseases in crystal guava plants can cause significant losses in fruit production. Some pests and diseases that can attack crystal guava plants include fruit flies, bagworms, whiteflies, anthracnose, and red rust. Therefore, accurate and timely disease diagnosis is crucial in controlling and managing these diseases. In this study, an Expert System for Disease Diagnosis in Crystal Guava Plants Using the Dempster Shafer Method-Based Web was developed. The Dempster Shafer method calculates the likelihood of a disease attacking crystal guava plants based on the density probability values of each symptom. The results produced by this expert system include the type of disease, solution, and the percentage probability value of the disease, with an average accuracy of 83.33%. Hence, it can be concluded that the system can diagnose diseases in crystal guava plants effectively.
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5

Ferrari, J. T., E. M. d. Nogueira, and A. J. T. dos Santos. "CONTROL OF RUST (PUCCINIA PSIDII) IN GUAVA (PSIDIUM GUAJAVA)." Acta Horticulturae, no. 452 (September 1997): 55–58. http://dx.doi.org/10.17660/actahortic.1997.452.8.

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6

da S. Machado, Patrícia, Morag Glen, Olinto L. Pereira, Arthur A. Silva, and Acelino C. Alfenas. "Epitypification of Puccinia psidii, Causal Agent of Guava Rust." Tropical Plant Pathology 40, no. 1 (2015): 5–12. http://dx.doi.org/10.1007/s40858-014-0002-8.

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7

Sanahuja, G., P. Lopez, A. J. Palmateer, and A. R. Chase. "Red Rust of Neoregelia Bromeliads Caused by a Parasitic Alga Cephaleuros parasiticus in Florida." Plant Health Progress 19, no. 1 (2018): 27–33. http://dx.doi.org/10.1094/php-11-17-0068-rs.

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A newly emerging disease affecting proprietary hybrids of Neoregelia bromeliads was found in nursery production throughout south Florida. Initial symptoms appeared as small, reddish leaf spots with signs of fungal-like growth on the abaxial leaf surface closely resembling a rust. Closer examination of diseased tissue revealed the growth to be an alga with morphological and molecular characteristics of Cephaleuros parasiticus. This genus of plant parasitic thalloid green algae occurs worldwide in tropical and subtropical regions. Isolates of C. parasiticus collected from Neoregelia hybrid bromeliads were compared with two common Cephaleuros spp. (C. parasiticus and C. virescens) causing leaf spots on guava (Psidium guajava) in south Florida. Further, pathogenicity tests were conducted on healthy Neoregelia hybrids using C. parasiticus isolates collected from diseased leaves of bromeliads and guava. Isolates from both sources produced symptoms consistent with those originally described as “red rust” on Neoregelia hybrids. Florida has 165 plant species belonging to 53 families that have been reported as hosts of Cephaleuros spp., but this is the first report on Neoregelia hybrid bromeliads.
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8

Burman, Erin, James D. Ackerman, and Raymond L. Tremblay. "Invasive Syzygium jambos trees in Puerto Rico: no refuge from guava rust." Journal of Tropical Ecology 33, no. 3 (2017): 205–12. http://dx.doi.org/10.1017/s026646741700013x.

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Abstract:Biological invasions can have negative consequences for resident biota, particularly when disease-causing organisms are involved. Austropuccinia psidii, or guava rust, has rapidly spread through the tropics affecting both native and non-native Myrtaceae. In Puerto Rico, the rust has become common on Syzygium jambos, an invasive tree native to South-East Asia. What are the drivers of infection, and do refugia exist across a heterogeneous landscape? We address these questions using species distribution modelling and beta regressions. The realized and potential distribution of Syzygium jambos is extensive. The model produced an AUC of 0.88, with land-use categories and precipitation accounting for 61.1% of the variation. Predictability of S. jambos is highest in disturbed habitats, especially in mountainous regions with high precipitation. All 101 trees surveyed and measured across Puerto Rico showed signs of infection to varying extents. Infection severity was consistently associated with annual mean temperature in all top beta regression models, but was also commonly associated with tree size and precipitation variables. We found no safe sites for S. jambos. Many trees were extremely unhealthy and some were dead, suggesting that S. jambos may soon become extinct on the island or reduced to persistent stump sprouts. Native vegetation may benefit from the local demise of S. jambos. While the rust has not jumped to native Myrtaceae, vigilance is required, as host-shifts have occurred in other tropical regions.
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9

Hanik, Nur Rokhimah, Rindi Diah Ayu Fitriani, Fiky Ariska Cahyanti, Desty Oktavianingtyas, and Tri Wahyuni. "Identification of Pests and Diseases Crystal Guava (Psidium guajava L.) in Ngargoyoso District, Karanganyar Regency." Jurnal Biologi Tropis 23, no. 3 (2023): 127–35. http://dx.doi.org/10.29303/jbt.v23i3.5021.

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Pests and diseases that attack Crystal Guava are the main obstacles in increasing agricultural productivity, as well as disrupting plant physiology and growth. The purpose of this study was to identify pests and diseases of crystal guava (Psidium guajava L). The study was conducted for 3 months, (June-August) 2022 in three crystal guava gardens in Gemawang Village, Dukuh, Ngargoyoso District, Karangayar. Data were obtained from observations of guava plant samples crystals and fruit attacked by pests and diseases, as well as interviews with owners and managers to obtain information about cultivation from planting to harvest. The results of the research found several pests of Crystal Guava (Psidium guajava L.), including: Frogs (Cynopterus spp.), Fruit flies (Bactrocera dorsalis), caterpillars (Setora nitens), ladybugs (Helopeltis sp.), sac caterpillars (Lepidopthera: Psychide), shoot caterpillars (Lepidoptera: Pyralidae), and mealybugs (Paracoccus marginatus). morphology due to nutrient deficiency (potassium/magnesium); black spot and fruit rot by the fungus Collectrichum gleosporioides Penz, fruit canker (scab) Pestalotiopsis psidii (pat) Mordue, red rust by the algae Chephaleurus virescens, wilt disease by the algae Fusarium sp, and the fungus Cercospora psidii. Treatment was carried out once a week, by applying NPK and gandasil fertilizers and spraying with regent 50 insecticides, Yasithrin and Fostin. as well as wrapping young fruit. Pests and diseases found in Crystal Guava plantations in this study can reduce production, therefore the results of this study can be used as information material for novice farmers who want to develop Crystal Guava picking tours both on plantations and in their yards.
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10

Doutoum, Assad S., Recep Eryigit, and Bulent Tugrul. "Classification of Guava Leaf Disease using Deep Learning." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 20 (October 23, 2023): 356–63. http://dx.doi.org/10.37394/23209.2023.20.38.

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A higher percentage of crops are affected by diseases, posing a challenge to agricultural production. It is possible to increase productivity by detecting and forecasting diseases early. Guava is a fruit grown in tropical and subtropical countries such as Chad, Pakistan, India, and South American nations. Guava trees can suffer from a variety of ailments, including Canker, Dot, Mummification, and Rust. A diagnosis based only on visual observation is unreliable and time-consuming. To help farmers identify plant diseases in their early stages, an automated diagnosis and prediction system is necessary. Therefore, we developed a deep learning method for classifying and forecasting guava leaf diseases. We investigated a dataset composed of 1834 leaf examples, separated into five categories. We trained the dataset using four different and generally preferred pre-trained CNN architectures. The EfficinetNet-B3 architecture outperformed the other three architectures, achieving 94.93% accuracy on the test data. The results ensure that deep learning methods are more successful and reliable than traditional methods.
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