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Relevant bibliographies by topics / Leaf and Fruit Diseases

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Academic literature on the topic 'Leaf and Fruit Diseases'

Author: Grafiati

Published: 18 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Leaf and Fruit Diseases"

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Anwar, Masrur, David Fahmi Abdillah, Ilham Basri, Yanuangga Galahartlambang, and Titik Khotiah. "Classification of Chili Fruit Diseases Using Deep Convolutional Neural Network Transfer Learning." Journal of Informatics Development 2, no. 2 (2024): 18–24. http://dx.doi.org/10.30741/jid.v2i2.1335.

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Chili peppers are among the highest-value agricultural commodities, often experiencing significant price fluctuations due to supply constraints. The rainy season frequently leads to crop failures caused by diseases affecting chili plants. Existing methods often struggle to accurately differentiate between similar symptoms on leaves and fruits, leading to misdiagnosis and ineffective disease management strategies. Early detection of these diseases, which manifest as symptoms on the leaves and fruits, is crucial for effective pest management. Common diseases include anthracnose, characterized by

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Nivetha., I., and M. Padmaa Dr. "Disease Detection in Tree Leaves and Fruits using Image Processing Techniques." Journal of Radio and Television Broadcast 3, no. 3 (2018): 24–28. https://doi.org/10.5281/zenodo.2293551.

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<em>India is the agriculture based country. Now a day, agricultural product yield is decrease for the reason of disease affect the plants like fungus, virus diseases. In this paper, we detect the tree leaves and fruits disease using image processing techniques. In this paper apple, grapes and pomegranate disease are detected. The apple diseases are apple scab, apple rot, Marssonina leaf blotch, black rot canker, apple mosaic. The grape diseases are black rot, powdery mildew, downy mildew, anthracnose, bacterial leaf spot, and rust. The pomegranate diseases are bacterial blight, aspergillus fru

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Islam, Sk Fahmida, Nayan Chakrabarty, and Mohammad Shorif Uddin. "Citrus leaf disease detection through deep learning approach." Bulletin of Electrical Engineering and Informatics 13, no. 4 (2024): 2643–49. http://dx.doi.org/10.11591/eei.v13i4.4521.

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The majority of people in the world directly or indirectly depend on agriculture. Plant diseases are a significant threat to agricultural production and food security. Due to its high nutritional value, citrus fruit is one of the most abundant fruits in the world. However, different diseases are responsible for degraded citrus production as well as financial losses to the farmers. Traditionally, visual observation by experts has been attended to diagnose plant diseases. Usually, plant leaf disease recognition methods mainly rely on expert experiences to manually extract the colour, composition

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Deepali Joshi, Et al. "Automatic Classification of Mango Leaf Disease based on Machine Learning and Deep Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1398–405. http://dx.doi.org/10.17762/ijritcc.v11i10.8683.

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Fruits are the essential source of nutrition for the human body. The fruit needs to be nurtured and cared for in order to remain healthy. Lack of upkeep, illnesses, blemishes, and fungi result in a considerable loss of produce and profit. One of the important and popular fruit that is consumed worldwide is Mango. It is a fragile fruit and is vulnerable to diseases that affects its quality and quantity. Manual inspection for diseases or infection is a tedious process and requires abundant resources such as time and labour. Manual inspection is inefficient and inaccurate. Automatic inspection on

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Harteveld, D. O. C., O. A. Akinsanmi, K. Chandra, and A. Drenth. "Timing of Infection and Development of Alternaria Diseases in the Canopy of Apple Trees." Plant Disease 98, no. 3 (2014): 401–8. http://dx.doi.org/10.1094/pdis-06-13-0676-re.

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Alternaria leaf blotch and fruit spot of apple caused by Alternaria spp. cause annual losses to the Australian apple industry. Erratic control using protectant fungicides is often experienced and may be due to the lack of understanding of the timing of infection and epidemiology of the diseases. We found that Alternaria leaf blotch infection began about 20 days after bloom (DAB) and the highest disease incidence occurred from 70 to 110 DAB. Alternaria fruit spot infection occurred about 100 DAB in the orchard. Fruit inoculations in planta showed that there was no specific susceptible stage of

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Truong, Hong H., Toyozo Sato, Seiju Ishikawa, Ayaka Minoshima, Takeaki Nishimura, and Yuuri Hirooka. "Three Colletotrichum Species Responsible for Anthracnose on Synsepalum dulcificum (Miracle Fruit)." International Journal of Phytopathology 7, no. 3 (2018): 89–101. http://dx.doi.org/10.33687/phytopath.007.03.2658.

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By 2016, fruit rot and two different leaf diseases (leaf spot and leaf blight) were found on Synsepalum dulcificum (miracle fruit) in Tokyo, Kanagawa and Kagoshima prefectures of Japan. From the lesions, abundant conidial masses and acervuli of three Colletotrichum species, two of which produced sexual state, were observed. We conducted a pathogenicity assay using these Colletotrichum species on healthy fruits and leaves of S. dulcificum. Our artificial inoculation tests showed symptoms of disease on tested fruit and leaf and indicated all three Colletotrichum species as causal agents of anthr

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Aksoy, Serra, Pinar Demircioglu, and Ismail Bogrekci. "Web-Based AI System for Detecting Apple Leaf and Fruit Diseases." AgriEngineering 7, no. 3 (2025): 51. https://doi.org/10.3390/agriengineering7030051.

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The present study seeks to improve the accuracy and reliability of disease identification in apple fruits and leaves through the use of state-of-the-art deep learning techniques. The research investigates several state-of-the-art architectures, such as Xception, InceptionV3, InceptionResNetV2, EfficientNetV2M, MobileNetV3Large, ResNet152V2, DenseNet201, and NASNetLarge. Among the models evaluated, ResNet152V2 performed best in the classification of apple fruit diseases, with a rate of 92%, whereas Xception proved most effective in the classification of apple leaf diseases, with 99% accuracy. T

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Rehman, Samra, Muhammad Attique Khan, Majed Alhaisoni, et al. "Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework." Computers, Materials & Continua 75, no. 1 (2023): 1179–94. http://dx.doi.org/10.32604/cmc.2023.035324.

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Jha, Sanjay Kumar, and Sita Lamichhane. "Fungal Diseases of Tomato in Kathmandu Valley." Journal of Nepal Biotechnology Association 4, no. 1 (2023): 72–74. http://dx.doi.org/10.3126/jnba.v4i1.53449.

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The infected parts of the tomato plant were collected from Jitpurphedi of Kathmandu, Nepal. The isolated fungi from the infected parts were Septoria lycopersici, Cladosporium oxysporum responsible for leaf spot, Phytophthora infestans and Rhizoctonia solani responsible for leaf blight, Cladosporium cladosporioides responsible for fruit rot, Leveilulla taurica responsible for powdery mildew and Plasmopara viticola responsible for Downey mildew disease. In the survey period, the highest incidence was found at leaf blight (30.08%) and the lowest at stem rot (4.64%). In the case of severity, the m

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Mudholakar, Sunita, Kavitha G, Kanaya Kumari K T, and Shubha G V. "Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 4043–51. http://dx.doi.org/10.22214/ijraset.2022.45868.

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Abstract: Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. Plant disease detection and classification are crucial long term agriculture. Manually monitoring citrus diseases is quite tough. As a result, image processing is used for designing an automated detection system for citrus plant diseases. Image acquisition, image preprocessing, image segmentation, feature extraction and classification are main processes in the citrus disease detection process. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, l

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Dissertations / Theses on the topic "Leaf and Fruit Diseases"

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Welker, Robert M. "White apple leafhopper affects apple fruit quality and leaf gas exchange." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-06112009-063712/.

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Saha, Arnab. "Studies on some leaf and fruit diseases of lagenaria siceraria (Molina) standl and their management." Thesis, University of North Bengal, 2017. http://ir.nbu.ac.in/hdl.handle.net/123456789/2649.

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Pretorius, Mathys Cornelius. "Epidemiology and control of Pseudocercospora angolensis fruit and leaf spot disease on citrus in Zimbabwe." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/20938.

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Thesis (MScAgric)--University of Stellenbosch, 2005.<br>ENGLISH ABSTRACT: Fruit and Leaf Spot Disease (FLSD) of citrus, caused by Phaeoramularia angolensis, is found only in 18 countries in Africa, the Comores Islands in the Indian Ocean and Yemen in the Arabian peninsula. The major citrus export countries in Africa are Morocco, South Africa, Swaziland, and Zimbabwe. Zimbabwe is the only country affected by FLSD. FLSD is a disease of major phytosanitary and economic importance and its devastating effect on citrus is highlighted by the fact that the damage is cosmetic, which renders the fr

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Abdurabi, Abdurabi Seif. "Studies of phaeoramularia fruit and leaf spot disease of citrus in Kenya." Thesis, University of Reading, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259502.

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Poulsen, Kristian Wermuth. "Effect of pre-bloom leaf defoliation on cluster morphology and disease pressure." Master's thesis, ISA-UL, 2015. http://hdl.handle.net/10400.5/12214.

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Mestrado Vinifera Euromaster - Instituto Superior de Agronomia - UL<br>Defoliation of grapevines have been shown to impact fruit set and fruit development, although the extent to and timing of which defoliation impacts fruit set and development is still being investigated. This is useful for the purpose of managing crop load and can be a useful tool for disease management. Currently, the understanding is that removal of leaves from the fruiting zone alters the source-sink balance, forcing the grapevine to down-prioritize flowering resulting in reduced fruit set, smaller clusters and less rot (

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Call, Robert E., and Michael E. Matheron. "Effective Management Tools for Septoria Leaf Spot of Pistachio in Arizona." College of Agriculture, University of Arizona (Tucson, AZ), 1998. http://hdl.handle.net/10150/220530.

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Septoria leaf spot was detected in the United States for the first time in 1964 within an experimental pistachio planting at Brownwood, Texas. The first observation of the same disease in Arizona pistachio trees did not occur until 1986. In 1988, a survey of the 2,000 acres of pistachio orchards in southeastern Arizona revealed a widespread incidence of the disease. Since the initial discovery of the disease, Septoria leaf spot has appeared annually in some of the Arizona pistachio acreage. The onset and severity of the disease is influenced by summer rainfall that occurs in this region. Disea

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Call, Robert E., and Michael E. Matheron. "Fungicidal Performance in Managing Septoria Leaf Spot of Pistachio in Arizona." College of Agriculture, University of Arizona (Tucson, AZ), 2000. http://hdl.handle.net/10150/223845.

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Septoria leaf spot was detected in the United States for the first time in 1964 within an experimental pistachio planting at Brownwood, Texas. The first observation of the same disease in Arizona pistachio trees did not occur until 1986. In 1988, a survey of the 2,000 acres of pistachio orchards in southeastern Arizona revealed a widespread incidence of the disease. Since the initial discovery of the disease, Septoria leaf spot has appeared annually in some Arizona pistachio acreage. The onset and severity of the disease is influenced by summer rainfall that occurs in this region. Pistachio tr

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Matheron, Michael E., Michael W. Kilby, and Robert Call. "Effect of Foliar Application of Benomyl on Severity of Septoria Leaf Spot on Pistachio in Southeastern Arizona." College of Agriculture, University of Arizona (Tucson, AZ), 1998. http://hdl.handle.net/10150/220574.

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The fungicide, benomyl (Benlate) was foliar applied by a commercial air blast sprayer at the rate of 1.0 lb. a.i. per acre in early to late August. Treatments varied with a number of applications i.e. one or two and were compared to an untreated control. Benomyl significantly reduced leaf necrosis surrounding nut clusters and the number of leaf spot lesions when compared to control. One or two applications were equally effective in controlling Septoria leaf spot.

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Ndo, Eunice. "Évaluation des facteurs de risque épidémiologique de la phaeoramulariose des agrumes dans les zones humides du Cameroun." Thesis, Montpellier, SupAgro, 2011. http://www.theses.fr/2011NSAM0034/document.

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La phaeoramulariose des agrumes (Pseudocercospora angolensis) attaque les agrumes en Afrique tropicale et constitue une menace pour les autres pays producteurs situés en zone tropicale. La lutte chimique est le seul moyen de lutte efficace contre cette maladie. Cependant, elle est couteuse et néfaste pour l'environnement. La mise en place de stratégies de lutte permettant de minimiser l'emploi de fongicides contre est donc nécessaire. La connaissance des facteurs de risque de la phaeoramulariose est une étape préliminaire à cette démarche. Le travail entrepris avait pour but de préciser, à l'a

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Lira, Bruno Silvestre. "Manipulation of leaf senescence and chlorophyll degradation aiming fruit improvement." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/41/41132/tde-28102017-114118/.

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Leaves are responsible for the majority of the fixed carbon in most plant species. Along leaf development, the photosynthetic capacity increases until the organ reaches maturity. Consequently, at the onset of senescence the leaves have the highest photosynthetic activity, then, as the chloroplasts are dismantled and the photosynthetic machinery is degraded, leaves gradually lose the rate of carbon assimilation. Although the capacity to fix carbon declines as senescence progresses, nutrient remobilization from macromolecule degradation nourishes the developing sink organs. In this regard, delay

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Books on the topic "Leaf and Fruit Diseases"

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Stebbins, Robert L. Using leaf analysis to diagnose nutrient disorders in tree fruits and small fruits. Oregon State University Extension Service, 1988.

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Hamilton, K. G. A. Cicadelles des arbres ornementaux et fruitiers du Canada. Agriculture Canada, 1985.

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Koch, Maryjo. Seed leaf flower fruit. Smithmark Publishers, 1998.

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Ontario. Ministry of Agriculture and Food. Leaf Analyses For Fruit Crops. s.n, 1988.

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International, Symposium on Virus and Virus-Like Diseases of Temperate Fruit Crops (17th 1997 Bethesda Md ). Fruit tree diseases. International Society of Horticultural Science, 1998.

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M, Ogawa J., ed. Stone fruit diseases. American Phytopathological Association, 1995.

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Ontario. Ministry of Agriculture and Food. Leaf analyses for fruit crop nutrition. s.n, 1990.

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Misra, A. K. Diseases of fruit crops. Indian Phytopathological Society, 2012.

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Mukerji, K. G., ed. Fruit and Vegetable Diseases. Kluwer Academic Publishers, 2004. http://dx.doi.org/10.1007/0-306-48575-3.

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College, Nova Scotia Agricultural, ed. Diseases of fruit plants. [s.n.], 1997.

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Book chapters on the topic "Leaf and Fruit Diseases"

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Mokal, Atul Bhimrao, Vaishnavi Avhad, Om Jadhav, Vishal Khamkar, Mansi Japtap, and Sumedh S. Ingle. "Pomegranate Leaf Fruit Disease Prediction Using Machine Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-5231-7_23.

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Nalini, C., N. Kayalvizhi, V. Keerthana, and R. Balaji. "Detection and Classification of Fruit Tree Leaf Disease Using Deep Learning." In Proceedings of Third Doctoral Symposium on Computational Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3148-2_30.

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Nandi, Rabindra Nath, Aminul Haque Palash, Nazmul Siddique, and Mohammed Golam Zilani. "Device-Friendly Guava Fruit and Leaf Disease Detection Using Deep Learning." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34619-4_5.

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Chowdhury, Mariam, Afrida Israt Jahan, Saima Murtuza, Tajbia Karim, and Md Jaber Al Nahian. "Early Detection and Classification of Fruit Leaf Diseases Using an Ensemble Approach Based on Transfer Learning." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8090-7_24.

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Naresh Kumar, S., Sankararao Majji, Tulasi Radhika Patnala, C. B. Jagadeesh, K. Ezhilarasan, and S. John Pimo. "Fruit and Leaf Disease Detection Based on Image Processing and Machine Learning Algorithms." In Expert Clouds and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2500-9_27.

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Anandamurugan, S., B. Deva Dharshini, J. Ayesha Howla, and T. Ranjith. "Deep Neural Network Model for Automatic Detection of Citrus Fruit and Leaf Disease." In Innovations in Bio-Inspired Computing and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96299-9_32.

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Mateke, Stanley M. "The Effect of Shade on Initial Growth, Development and Occurrences of Leaf Diseases on Wild Indigenous Fruit Trees." In Combating Desertification with Plants. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1327-8_21.

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Vishunavat, Karuna, Kuppusami Prabakar, and Theerthagiri Anand. "Seed Health: Testing and Management." In Seed Science and Technology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5888-5_14.

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AbstractHealthy seeds play an important role in growing a healthy crop. Seed health testing is performed by detecting the presence or absence of insect infestation and seed-borne diseases caused by fungi, bacteria, and viruses. The most detrimental effect of seed-borne pathogens is the contamination of previously disease-free areas and the spread of new diseases. Sowing contaminated or infected seeds not only spreads pathogens but can also reduce yields significantly by 15–90%. Some of the major seed-borne diseases affecting yield in cereals, oilseeds, legumes, and vegetables, particularly in

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Sharma, Saurabh, Gajanand Sharma, Ekta Menghani, and Anupama Sharma. "A Comprehensive Review on Automatic Detection and Early Prediction of Tomato Diseases and Pests Control Based on Leaf/Fruit Images." In Lecture Notes in Networks and Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22018-0_26.

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Morgan, Lynette. "Hydroponic production of selected crops." In Hydroponics and protected cultivation: a practical guide. CABI, 2021. http://dx.doi.org/10.1079/9781789244830.0011a.

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Abstract While there is a wide range of potentially profitable crops which can be grown in hydroponics under protected cultivation, greenhouse production is dominated by fruiting crops such as tomatoes, cucumber, capsicum and strawberries, and vegetative species such as lettuce, salad and leafy greens, herbs and specialty crops like microgreens. This chapter summarizes information on a selected range of common hydroponic crops to give basic procedures for each and an outline of the systems of production. These crops include tomato, capsicum or sweet bell pepper, cucumber, lettuce and other sal

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Conference papers on the topic "Leaf and Fruit Diseases"

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Wang, Yizong, Zhengrong Xiao, Dengxun Sun, and Yefeng Liang. "Mixed detection of grape leaf and fruit diseases based on Yolov5s." In International Conference on Mechatronics and Intelligent Control (ICMIC 2024), edited by Kun Zhang and Pascal Lorenz. SPIE, 2025. https://doi.org/10.1117/12.3047494.

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Rank, Yashkumar, and Kruti Sutariya. "A Review on Papaya Leaf and Fruit Disease Classification Techniques." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841766.

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Joshi, Akanksha, Arjun Badola, and Rashmi Saini. "Deep Learning Based Fruit Crop Disease Classification Using Plants Leaf Imagery." In 2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP). IEEE, 2024. http://dx.doi.org/10.1109/icecsp61809.2024.10698492.

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K S N V, Someswara Rao, Chiriki Usha, Sesham Uday Kiran, T. Hari Priya, B. Krishna Sai, and Ramana babu challapalli. "Vegetable & Fruit Leaf Disease Detection Using DCNN & Enhanced Dataset." In First International Conference on Computer, Computation and Communication (IC3C-2025). River Publishers, 2025. https://doi.org/10.13052/rp-9788743808268a066.

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Chanakya, G., Repala Harini, Harsh Satish Kadam, N. Uday Kiran, and Nemuri Chandrakanth. "An Automated Leaf & Fruit Disease Prediction using Transfer Learning and Recommendations." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00052.

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Belmir, Meroua, Wafa Difallah, and Abdelkader Ghazli. "A Reliable Apple Leaf Disease Identification Using a Deep Learning-Based MobileNetV2 to Safeguard Apple Fruit Safety." In 2024 4th International Conference on Embedded & Distributed Systems (EDiS). IEEE, 2024. https://doi.org/10.1109/edis63605.2024.10783370.

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Mushtaq, Maroof Ul, Sanjay Singla, and Sandeep Singh Kang. "Deep Learning based Model for early detection of disease in Apple Leaf and Fruit with Severity level and Recommendation for Diagnosis." In 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE). IEEE, 2025. https://doi.org/10.1109/rmkmate64874.2025.11042551.

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Ghosh, Urmi, Sakib Rokoni, Md Ataur Rahman, Md Sadekur Rahman, and Md Tarek Habib. "CNN Modeling for Recognizing Rice Leaf Diseases." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725764.

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Fan, Yi, and Xing Zhang. "Classification of Plant Leaf Diseases Based on EfficientNet." In 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM). IEEE, 2024. https://doi.org/10.1109/ispcem64498.2024.00046.

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Saini, Archana, Kalpna Guleria, and Shagun Sharma. "Automated Classification of Mustard Leaf Diseases with VGG19." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877486.

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Reports on the topic "Leaf and Fruit Diseases"

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Reisch, Bruce, Avichai Perl, Julie Kikkert, Ruth Ben-Arie, and Rachel Gollop. Use of Anti-Fungal Gene Synergisms for Improved Foliar and Fruit Disease Tolerance in Transgenic Grapes. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7575292.bard.

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Original objectives . 1. Test anti-fungal gene products for activity against Uncinula necator, Aspergillus niger, Rhizopus stolonifer and Botrytis cinerea. 2. For Agrobacterium transformation, design appropriate vectors with gene combinations. 3. Use biolistic bombardment and Agrobacterium for transformation of important cultivars. 4. Characterize gene expression in transformants, as well as level of powdery mildew and Botrytis resistance in foliage of transformed plants. Background The production of new grape cultivars by conventional breeding is a complex and time-consuming process. Transfer

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Cohen, Roni, Kevin Crosby, Menahem Edelstein, et al. Grafting as a strategy for disease and stress management in muskmelon production. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7613874.bard.

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The overall objective of this research was to elucidate the horticultural, pathological, physiological and molecular factors impacting melon varieties (scion) grafted onto M. cannonballus resistant melon and squash rootstocks. Specific objectives were- to compare the performance of resistant melon germplasm (grafted and non-grafted) when exposed to M. cannoballus in the Lower Rio Grande valley and the Wintergarden, Texas, and in the Arava valley, Israel; to address inter-species relationships between a Monosporascus resistant melon rootstock and susceptible melon scions in terms of fruit-set,

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Ori, Naomi, and Mark Estelle. Specific mediators of auxin activity during tomato leaf and fruit development. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597921.bard.

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The plant hormone auxin is involved in numerous developmental processes, including leaf and fruit development. The tomato (Solanumlycopersicum) gene ENTIRE (E) encodes an auxin-response inhibitor from the Aux/IAA family. While most loss-offunction mutations in Aux/IAA genes are similar to the wild type due to genetic redundancy, entire (e) mutants show specific effects on leaf and fruit development. e mutants have simple leaves, in contrast to the compound leaves of wild type tomatoes. In addition, e plants produce parthenocarpic fruits, in which fruit set occurs independently of fertilization

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Valverde, Rodrigo A., Aviv Dombrovsky, and Noa Sela. Interactions between Bell pepper endornavirus and acute viruses in bell pepper and effect to the host. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598166.bard.

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Based on the type of relationship with the host, plant viruses can be grouped as acute or persistent. Acute viruses are well studied and cause disease. In contrast, persistent viruses do not appear to affect the phenotype of the host. The genus Endornavirus contains persistent viruses that infect plants without causing visible symptoms. Infections by endornaviruses have been reported in many economically important crops, such as avocado, barley, common bean, melon, pepper, and rice. However, little is known about the effect they have on their plant hosts. The long term objective of the propose

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Wilson, Charles, and Edo Chalutz. Biological Control of Postharvest Diseases of Citrus and Deciduous Fruit. United States Department of Agriculture, 1991. http://dx.doi.org/10.32747/1991.7603518.bard.

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The objectives of this research were to develop control measures of postharvest diseases of citrus and deciduous fruits by using naturally-occurring, non-antibiotic-producing antagonists; study the mode of action of effective antagonists and optimize their application methods. Several antagonists were found against a variety of diseases of fruits and vegetables. One particularly effective yeast antagonist (US-7) was chosen for more in-depth studies. This antagonist outcompetes rot pathogens at the wound site for nutrients and space; it is better adapted than the pathogen to extreme environment

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Munkvold, Gary P., Charlie Martinson, and John M. Shriver. Fungicidal Control of Leaf Diseases in High-Oil Hybrid Corn, 2000. Iowa State University, Digital Repository, 2001. http://dx.doi.org/10.31274/farmprogressreports-180814-242.

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Portz, Dennis N., Leah B. Riesselman, Crystal Seeley, Paul Beamer, and Gail R. Nonnecke. Effects of Leaf Removal on Fruit Quality of Wine Grapes Grown in Iowa. Iowa State University, Digital Repository, 2011. http://dx.doi.org/10.31274/farmprogressreports-180814-153.

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Portz, Dennis N., Leah B. Riesselman, Crystal Seeley, Paul Beamer, and Gail R. Nonnecke. Effects of Leaf Removal on Fruit Quality of Wine Grapes Grown in Iowa. Iowa State University, Digital Repository, 2012. http://dx.doi.org/10.31274/farmprogressreports-180814-456.

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Katan, Jaacov, James DeVay, Ezra Shabi, and Yacov Pinkas. Postplant Control of Soilborne Diseases of Fruit Tree Crops by Soil Solarization. United States Department of Agriculture, 1992. http://dx.doi.org/10.32747/1992.7600055.bard.

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Schaffer, Arthur A., D. Mason Pharr, Joseph Burger, James D. Burton, and Eliezer Zamski. Aspects of Sugar Metabolism in Melon Fruit as Determinants of Fruit Quality. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7568770.bard.

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The cucurbit family, including melon, translocates the galactosyl-sucrose oligosaccharides, raffinose and stachyose, in addition to sucrose, from the source leaves to the fruit sink. The metabolism of these photoassimilates in the fruit sink controls fruit growth and development, including the horticulturally important phenomenon of sucrose accumulation, which determines melon fruit sweetness. During this research project we have characterized the complete pathway of galactosyl sucrose metabolism in developing fruit, from before anthesis until maturity. We have also compared the metabolic path

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