Non-Destructive Assessment of Quality and Maturity of Mangoes Based on Image Analysis

Total Page:16

File Type:pdf, Size:1020Kb

Non-Destructive Assessment of Quality and Maturity of Mangoes Based on Image Analysis MSc. Thesis (MSCV) Department of Applied Computer Science University of Bourgogne Non-destructive assessment of quality and maturity of mangoes based on image analysis Kumar Ankush A Thesis submitted in July 2020 for the Degree of MSc in Computer Vision (VIBOT/MSCV) Master thesis supervised by Dr. Emile Faye and Dr. Julien Sarron of the UPR HortSys of CIRAD. Abstract Quality and maturity estimation of mangoes are important for the organization of harvesting date and post-harvest conservation. Although extensive fruit quality estimations exist, they are mostly destructive in nature and available tools for non-destructive estimation are limited. Maturity estimation rely on visual inspection of the fruit, in-hand feel or destructive measure- ment based on quality assessment. Thus, non-destructive tools for an accurate estimation of the quality and the maturity of the fruit have yet to be developed, especially for smallhold- ers. The aim of this study was to develop a tool for non-destructive assessment of quality and maturity of mangoes based on image analysis. This experiment studied 1040 lateral RGB images of 520 mangoes of di↵erent stages of maturity and harvested in two orchards in West Africa. Upon performing digital image segmentation on the images of mangoes, six image fea- tures were calculated with the use of digital image processing functions in MATLAB and four destructive features were taken in consideration. Then, correlations between destructive and non-destructive features of mangoes were explored. No research is ever quite complete. It is the glory of a good bit of work that it opens the way for something still better, and this repeatedly leads to its own eclipse... Mervin Gordon Contents Acknowledgments iv 1 Background 1 1.1 Introduction....................................... 1 1.2 Problemdefinition ................................... 6 1.3 Stateoftheart..................................... 7 2 Methodology 8 3Results 12 3.1 First part of the objective: segmentation of images . 12 3.2 Second part of the objective: classification of mango images into sunny and shade side ........................................... 12 3.3 Third part of the objective: calculation of color and shape image features . 13 4 Discussions 15 A The first appendix 17 Bibliography 23 i List of Figures 1.1 Production of Mangoes, mango steens and guavas in West Africa . 3 1.2 Production of Mangoes, mango steens and guavas in Senegal [3] . 4 1.3 Production of Mangoes, mango steens and guavas in Cote d’Ivoire [3] . 5 2.1 Mango being drilled for pulp collection . 10 2.2 Kent Maturity Scale for Mangoes . 11 A.1 Mango Sample Batch and segmentation images . 18 A.2 Mango Sample Batch and segmentation images . 19 ii List of Tables 3.1 Batch accuracy of classification . 14 iii Acknowledgments I take this opportunity to express my sincere and heartfelt gratitude to my Master’s thesis supervisor Dr. Emile Faye for believing in my capabilities and encouraging me to understand and link complex concepts. I would like to thank Dr. Julien Sarron. I really appreciate your constant support through- out broadening my knowledge and professional development. You taught me to be like a sponge and absorb all the knowledge that comes along the way and that there is no evidence or measure of failure or success if you do not even try. I am thankful to Dr. Lew and Dr. Fofi for their guidance during the coursework. I am highly indebted to Herma, Alice, Elizabeth and Aurelie for all the administrative and official assistance. Lastly, I express gratitude towards my dear VIBOT batchmates for their unconditional support throughout this Master’s Programme. I will not be able to thank enough my parents who always strenghthen me by teaching to stand tall and fight hard to find the way through the world of uncertainties. I feel blessed to have my wife Pratima Gurung’s immense support and love throughout my journey. iv Chapter 1 Background 1.1 Introduction There is a pressing need for fruit maturity estimation for increased food security and less wastage of perishable food in either storage of transportation. According to an estimation of Food and Agriculture Organization of the United Nations (FAO), in 2011 a third or 1.3 billion metric tons of the world’s food was lost or wasted each year [5] with about 88 million metric tons wasted in Europe [6] with associated cost around 148 billion Euros. In order to reduce the food waste proper grading of food is important to decrease food loss and increase profitability of the farmers. Skin colour is an acceptable and recognized maturity indexing technique for many fruits [20]. Since most fruits show demonstrable changes on the outside with change in their physio-chemical properties, such as hormone secretion and change in polarization of reflected light, change in skin colour is however the most easily perceivable one. Having an index-scale to grade the change becomes useful in plucking, handling and sorting and thus becomes a key step in cultivation management. Producers also strive to prevent products with defective colors from entering the market, as well as to ensure that individual products are packed in batches of similar colour. It is crucial to identify maturity index of fruits since immature fruits exhibit erratic ripening behavior if handled improperly [15]. It almost certainly leads to improper flavor, aroma and texture of the fruit, ultimately leading to its rejection either during transport or in retail. By addressing these key concerns will impact in three big ways. First, it will reduce food loss. Streamlining the process of harvesting, picking of fruits, appropriate packaging and trans- 1 Chapter 1: Background 2 port will result in less food which is lost before it can be served on a plate. Second, it will reshape the workforce and organize the logistics efficiently [16]. Knowing certain packages must be transported on a priority and carefully, will give access to prioritize specific markets and conveyance. Third, it will optimize long term storage and retail. Fruits can be sold at priority in retail depending on their maturity, thus reducing loss to the seller and leading to more value delivered to the consumer. However, these estimations aren’t easy to perform by humans. In a study by Paulus et al. , even trained operators faced difficulties in reproducing the estimation of quality where parameters such as shape, size and colour were involved and termed it as ‘inconsistency’ [26]. As the number of parameters increased, which is an increase in complexity and decision making process, the error in classification also increased. Area of Computer Vision o↵ers powerful tools for automated inspection of fruits and vegeta- bles. The artificial vision system is capable of achieving beyond human capacity in evaluating long-term process accurately in much shorter time through image capturing devices. It can simplify tedious monitoring process flow that take complex operations to be performed. With use of devices which can ‘see’ electromagnetic waves outside of visible range have brought more data points, thus adding more features for accurate analysis [35]. Hyperspectral analysis can even ‘look into’ food damage through bruise of infection under the peel, helping to isolate such food and hence controlling the spread of disease or release of excess plant hormones from bruised parts of the food. Choi KH et al. in 1994 in accordance with United States Department of Agriculture pro- posed a classification of tomatoes in 6 classes [7]. Similar attempts were made by Liming X et al. in 2010 for classification of strawberries [17]. Martynenko et al. in 2008 developed a process for estimating changes in density and porosity of ginseng roots during drying, thus eliminat- ing use of scanning electron microscope in imaging [19].The ultimate aim of many computer vision-based inspection systems is to estimate one or more features of the product of interest at a given time and compare them to the consistency that is usually associated with maturity, absence of deformities and blemishes, etc. Other systems are designed to determine the prod- uct’s evolution over time to determine whether a given process is valid. Mango (Mangifera indicaL.) is the world’s 5th most produced fruit (50 metric tonnes) [9], grown mainly in semi-arid, tropical and subtropical areas. The fruit is produced mostly by In- dia (16 Mt) and China (4.3 Mt) in South-East Asia. Mexico (23% of world exports), Brazil (14.3%) and Peru (10.3%) are the main exporting countries [9]. All of Africa accounts for 9% 3 1.1 Introduction while West Africa 4% (1.5 Mt) of the world’s mango production [4]. In West Africa, however, production has doubled over the last decade, marking the country with the highest rate of growth in production in the world. Figure 1.1: Production of Mangoes, mango steens and guavas in West Africa Cote d’Ivore and Senegal combined are major mango producing countries in West Africa with 93,267 tonnes and 133,636 tonnes of produce in 2018 respectively [10]. Of these, around 10,000 and 60,000 tonnes were exported to the Europe [4]. A chart representation of the produc- tion of mango, mangosteens and guavas as reported by the FAO has been included as Figure 1.2 and Figure 1.3. Mangoes are one of the climacteric fruits – fruits that ripen, become softer and sweeter post-harvest. Such fruits are not considered to be of desired eating quality at the time of initial maturity. It takes a ripening period to develop desired taste and texture at the time of con- sumption. The maturing process is driven by genetic, environmental and biochemical events resulting in biochemical and physical changes such as loss of ascorbic acid [14], increase in total soluble solids including sugar and starch [25]; physical changes such as weight, firmness, size and color [24], and aroma changes, nutritional value and flavour.
Recommended publications
  • Value Chain Analysis for Processed Fruits from Burkina Faso, Mali and Ivory Coast
    CBI Ministry of Foreign Affairs Value Chain Analysis for Processed Fruits from Burkina Faso, Mali and Ivory Coast Commissioned by The Centre for the Promotion of Imports from developing countries (CBI) Agri-Logic August 2019 Value Chain Analysis for Processed Fruits from Burkina Faso, Mali and Ivory Coast Value Chain Analysis for CBI Final edited version 12 August 2019 Prepared by: Herma Mulder Sanne Steemers Jean Bosco Dibouloni Jacques Tamini Mohamed Ali Niang 1 Executive summary Background and scope This value chain analysis was commissioned by CBI (Centre for the Promotion of Imports from developing countries) in order to identify the most promising product market combinations for processed fruit from Burkina Faso, Côte d’Ivoire and Mali. Export market competitiveness While tropical dried fruits and tropical frozen fruits are still relatively small compared to the total processed fruit market size, we see that tropical fruits are gaining market share from native European fruits. Preserved fruits (mainly canned pineapple), fruit juices, concentrates (for juices) and coconut oil are larger markets, but they are stable or in some cases declining. Purées for the baby food segment and coconut derivatives other than coconut oil are interesting growth segments when the high quality and food safety standards required are achieved. For all processed fruits market segments, the same countries appear as trade hubs for the European market: The United Kingdom, The Netherlands, Germany, France, Belgium and Italy. Market trends provide opportunities and threats for the development of a processed fruits segment. Consumer demands for healthy and sustainable products are increasing. The shift in consumer dietary patterns and the growing vegan population is expected to drive the growth of the processed fruit market.
    [Show full text]
  • Diversity of a Large Collection of Natural Populations of Mango (Mangifera Indica Linn.) Revealed by Agro-Morphological and Quality Traits
    diversity Article Diversity of a Large Collection of Natural Populations of Mango (Mangifera indica Linn.) Revealed by Agro-Morphological and Quality Traits Cuixian Zhang y, Dehong Xie y, Tianqi Bai, Xinping Luo, Faming Zhang, Zhangguang Ni * and Yufu Chen * Institute of Tropical and Subtropical Cash Crops, Yunnan Academy of Agricultural Sciences, Baoshan 678000, China; [email protected] (C.Z.); [email protected] (D.X.); [email protected] (T.B.); [email protected] (X.L.); [email protected] (F.Z.) * Correspondence: [email protected] (Z.N.); [email protected] or [email protected] (Y.C.) These authors contributed equally to this work. y Received: 11 December 2019; Accepted: 3 January 2020; Published: 11 January 2020 Abstract: Collection, characterization and utilization of genetic resources are crucial for developing varieties to meet current and future needs. Although mango is an economically important fruit tree, its genetic resources are still undocumented and are threatened in their natural habits. In this study, the variability of 452 mango accessions from three regions in China (Nujiang, Lancang river and Honghe) was assessed using 41 descriptors including qualitative and quantitative traits, with the aim to identify mango accessions with excellent agronomic and quality traits. To this end, descriptive and multivariate analyses were performed. Based on Shannon–Weaver diversity index, qualitative traits including pericarp color, fruit aroma, flesh color, and fruit flavor recorded the highest variability in the germplasm. Fruit related traits including pulp weight, peel weight, and fruit weight were the most diverse traits in the germplasm with a high coefficient of variation (CV > 40%). Significant differences (MANOVA test, p < 0.000) were observed among the three regions for most of the quantitative traits.
    [Show full text]
  • Handbook for Agricultural and Fishery Products Import Regulations 2009
    Handbook for Agricultural and Fishery Products Import Regulations 2009 February 2010 CONTENTS I. Products ....................................................................................................................1 1. Live Animals .............................................................................................................2 2. Meat and Prepared Products ...................................................................................7 3. Other Animal Products ...........................................................................................13 4. Fishery Products and Prepared Products .............................................................18 5. Dairy Products, etc. ...............................................................................................24 6. Plants, Resins and Vegetable Juices, etc. ............................................................28 7. Vegetables, Fruits and Prepared Products ............................................................33 8. Cereals and Prepared Products ............................................................................38 9. Sugars, Cocoa and Prepared Products .................................................................44 10. Spices ....................................................................................................................47 11. Oil Seeds and Prepared Products .........................................................................50 12. Various Prepared Foods ........................................................................................54
    [Show full text]
  • Seasonal Calendar: Fruits
    Seasonal Calendar: Fruits FRUITS TYPE EU OVERSEAS JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Apple Braeburn AT, DE, IT, NL AR, NZ, US Cox Orange DE, NL - Elstar BE, DE, NL - Fuji FR, IT AR, NZ, US Granny Smith IT AR, US Jonagold BE, DE, NL - Red Delicious FR, IT AR, US Royal Gala DE, IT, NL AR, CL, NZ, US Apricot Diff. varieties ES, FR, GR, IT CL, NZ, TN, TR, ZA Avocado Ettinger - IL Fuerte ES, IT CL, EG, IL, KE, MX, PE, ZA Hass/Black Sensation ES, IT CL, CO, EG, IL, KE, MX, PE, ZA Pinkerton - IL Reed - IL Banana Baby Banana - CO, CR, DO, EC, GT, PE Cavendish - CO, DO, EC, GT, PE Cooking Banana (Plantains) - CO, DO, EC, GT, PE, TH Red Banana - CR, EC, ID, KE, PE Blackberry Diff. varieties BE, DE, FR, HU, IT, NL CL, GT, MX, NZ, US Blueberry Diff. varieties DE, ES, FR, IT AU, BY, CL, NZ, PE, ZA Cherimoya Diff. varieties ES AU, CL, EC, EG, IL, KE, NZ, PE, US Cherry Morello DE, HU, PL TR, US Sweet DE, IT TR, US Coconut Diff. varieties - CI, DO, HN, LK, PH Cranberry Diff. varieties IT CA, CL, US Currant Black DE, FR, NL CL Red BE, DE, NL CL Date Diff. varieties IT EG, IL, IR, TN, US Dragon fruit (Pitaya) Diff. varieties - CO, EC, IL, NI, TH, VN Fig Diff. varieties ES, IT BR, IL, TN, TR Ginger Diff. varieties - CN, PE AR Argentina CA Canada CO Colombia (IPD) EG Egypt (IPD) GM Gambia IT Italy MG Madagaskar NZ New Zealand RU Russian Federation UA Ukraine (IPD) AU Australia CD Democratic Republic CR Costa Rica ES Spain GT Guatemala IR Iran MX Mexico PA Panama SN Senegal UG Uganda AT Austria of the Congo CY Cyprus ET Ethiopia (IPD) HN Honduras
    [Show full text]
  • A New Approach for Analyzing and Interpreting Data on Fruit Drop In
    HORTSCIENCE 49(12):1498–1505. 2014. of the physiological fruit drop’’ when fruits have attained 10% or 30% of final fruit size, respectively. The premature fruit drop stages A New Approach for Analyzing and have been named invariably and there is also no common agreement on the number of drop Interpreting Data on Fruit Drop stages as well as the onset and duration of each. Dahshan and Habib (1985) originally in Mango described three distinct stages of premature fruit drop of mango and this classification was Michael H. Hagemann1, Malte G. Roemer, Julian Kofler, also used in the review of Singh et al. (2005). Martin Hegele, and Jens N. Wunsche€ The first stage is referred to as ‘‘post-setting Section of Crop Physiology of Specialty Crops, University of Hohenheim, drop’’ and ceases 60 d after ‘‘fruit set’’ (BBCH-scale 619). The second stage is Emil-Wolff-Str. 25, 70599 Stuttgart, Germany termed ‘‘mid-season drop,’’ characterized by Additional index words. fruit retention, intercropping, irrigation, Mangifera indica, plant a duration of 15 d with lesser intensity than growth regulator during the ‘‘post-setting drop.’’ The third stage is the ‘‘pre-harvest drop’’ with only moderate Abstract. Mango yields are frequently reduced by premature fruit drop, induced by plant losses. stresses during the fruit set period in response to unsuitable climatic or crop management These descriptions of premature fruit drop, conditions. There are varying strategies for assessing premature fruit drop, which render commonly found in the literature, represent the comparison and interpretation of published data difficult to draw general conclu- some considerable limitations.
    [Show full text]
  • Mangifera Indica CV. 'Nam Dok Mai Si Thong'
    E3S Web of Conferences 187, 04006 (2020) https://doi.org/10.1051/e3sconf /202018704006 TSAE 2020 Overall precision test of near infrared spectroscopy on mango fruits (Mangifera indica CV. ‘Nam Dok Mai Si Thong’) by on-line and off-line systems Wachiraya Lekhawattana1*, and Panmanas Sirisomboon2 1King Mongkut’s Institute of Technology Ladkrabang, Department of Agricultural Engineering, Faculty of Engineering, Bangkok, Thailand. 2King Mongkut’s Institute of Technology Ladkrabang, NIR Spectroscopy Research Center for Agricultural Products and Foods, Department of Agricultural Engineering, Faculty of Engineering, Bangkok, Thailand. Abstract. The near infrared (NIR) spectroscopy both on-line and off-line scanning was applied on mango fruits (Mangifera indica CV. ‘Nam dok mai- si Thong’) for the overall precision test. The reference parameter was total soluble solids content (Brix value). The results showed that the off-line scanning had a higher accuracy than on-line scanning. The scanning repeatability of the off-line and on-line systems were 0.00199 and 0.00993, respectively. The scanning reproducibility of the off-line and online systems were 0.00279 and 0.00513, respectively. The reference of measurement ͦ repeatability was 0.2. The maximum coefficient of determination (R ΛΏΦ) of the reference measurement was 0.894. 1 Introduction Mango is an important economic crop, which can generate income for farmers in Thailand because of consumption demand in both domestic and international markets. According to the statistics of mangoes exports in 2017, The total production throughout was 2.017 million tons [2]. The most exported mango species include nam dok mai, Khiao Sawiei, Hnuk Klangwan, Chok Anan, Rad and Aok Rong, respectively.
    [Show full text]
  • Variability in Fruit Characteristics of Chokanan Mango (Mangifera Indica L.) [Kepelbagaian Ciri-Ciri Buah Mangga Chokanan (Mangifera Indica L.)]
    J. Trop. Agric. and Fd. Sc. 46(1)(2018): 55 – 63 F. Abdullah Variability in fruit characteristics of Chokanan mango (Mangifera indica L.) [Kepelbagaian ciri-ciri buah mangga Chokanan (Mangifera indica L.)] F. Abdullah1 1Director General’s Office, MARDI Arau, Lot PT 3747, Jalan Tambun Tulang, 02600 Arau, Perlis, Malaysia Abstract This study was conducted with the aim to evaluate the characteristics of Chokanan mango at harvest. The Chokanan fruits were harvested from selected trees in commercial mango plot at Sintok MARDI Research Station, Kedah, Malaysia. Data on the fruit characteristics such as weight, length (L), width (W), thickness (T) and total soluble solid (TSS, °Brix) were measured and recorded for 13 weeks. The results revealed high significant differences (p <0.001) in terms of recorded weight, W, L, T, size (W x L x T) and TSS of the Chokanan fruits among the selected trees. The mean value of the fruits weight at harvest ranged from 120 to 380 g and TSS ranged from 14.5 ºBrix to 22.8 ºBrix. In the present study, the distribution of fruits weight was skewed to the left indicating larger percentage of smaller fruits, but normal distribution was recorded for the TSS. Apart from that, high significant correlation (R2 >0.80) was found in the weight and size, L, W and T of the fruits. However, the weight and size of Chokanan were inversely correlated with TSS. The coefficient of variation (cv) differed among the harvested trees with the highest were recorded on weight and size of fruits (cv = 9.5 and 11.4, respectively).
    [Show full text]
  • Floral Biology and Pollen Viability of Some Mango (Mangifera Indica L.) Cultivars
    Int.J.Curr.Microbiol.App.Sci (2020) 9(6): 2390-2400 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 6 (2020) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2020.906.293 Floral Biology and Pollen Viability of Some Mango (Mangifera indica L.) Cultivars Sunil Kumar Mandal1, Kumari Karuna1*, Ashwini Kumar1, Abhay Mankar2 and Sanjay Sahay1 1Department of Horticulture (Fruit & Fruit Tech.), Bihar Agricultural College, Sabour, Bihar, India 2Directorate of Extension Education, Bihar Agricultural University, Sabour, Bihar, India *Corresponding author ABSTRACT The experiment was conducted at Department of Horticulture (Fruit and Fruit Tech.), Bihar Agricultural College under Bihar Agricultural University during the year 2017-18 to show the floral biology and pollen viability of some cultivars of K e yw or ds mango. The results indicate that the duration of panicle initiation was highest (45 Mango cultivars, days) in Langra In respect of panicle length maximum (38.4 cm) was recorded in pollen viability, Zardalu. Highest flowering period was found in Hussan-e-ara (27 days). Highest anthesis, sex ratio per cent of anthesis occurred during 6.00 AM to 10.00 AM in all cultivars. Data Article Info regarding to number of flowers per panicle were maximum in Langra (1003). In respect of number of male flowers it was found significantly maximum in Langra Accepted: (705). Number of hermaphrodite flowers was also maximum (298) in Langra. Sex 18 May 2020 Available Online: ratio is an important parameter for determination of fruiting behaviour and 10 June 2020 ultimately yield.
    [Show full text]
  • Correlation Between Inflorescence Phenological Events of Mango (Mangifera Indica Cv
    Journal of Experimental Agriculture International 25(3): 1-12, 2018; Article no.JEAI.43153 ISSN: 2457-0591 (Past name: American Journal of Experimental Agriculture, Past ISSN: 2231-0606) Correlation between Inflorescence Phenological Events of Mango (Mangifera indica cv. Kent) and Some Climatic Parameters in Côte d'Ivoire Kouakou Kouassi Léopold1*, Kone Tchoa1, N'da Adopo Achille2 and Kouakou Tanoh Hilaire1 1Laboratoire de Biologie et Amélioration des Productions Végétales, UFR Sciences de la Nature, Université Nangui Abrogoua, 02 BP 801 Abidjan 02, Côte d’Ivoire. 2Centre National de Recherche Agronomique, Station de Lataha (Korhogo), 01 BP 1740 Abidjan Côte d’Ivoire. Authors’ contributions This work was carried out in collaboration between all authors. Author KKL designed the study, performed the statistical analysis, wrote the protocol and wrote the first draft of the manuscript. Authors NAA and KTH managed the analyses of the study. Author KT managed the literature searches. All authors read and approved the final manuscript. Article Information DOI: 10.9734/JEAI/2018/43153 Editor(s): (1) Lanzhuang Chen, Professor, Laboratory of Plant Biotechnology, Faculty of Environment and Horticulture, Minami Kyushu University, Miyazaki, Japan. Reviewers: (1) Kabi Pokhrel, Tribhuvan University, Nepal. (2) Raúl Leonel Grijalva-Contreras, Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias, Mexico. Complete Peer review History: http://www.sciencedomain.org/review-history/25866 Received 19th May 2018 th Original Research Article Accepted 24 July 2018 Published 13th August 2018 ABSTRACT Aims: It was aimed to investigate the relationship between two climatic parameters, temperature and relative humidity, and the flower-cutting interval of mangoes tree cv. Kent Study Design: The experimental design used was the completely randomized design with triplicate.
    [Show full text]
  • Use of Potassium Nitrate on Mango Flowering
    USE OF POTASSIUM NITRATE ON MANGO FLOWERING Mike A. Nagaol and Melvin S. Nishina2 lDepartment of Horticulture and 2Cooperative Extension Service, Hawaii County College of Tropical Agriculture and Human Resources University of Hawaii at Manoa The mango (Mangifera indica L.), a member of Elisa and Davenport 1992). It was concluded that the Anarcardiaceae, is a popular fruit of the water stress was not responsible for flower tropics and occupies a position in the tropics induction, but could enhance the response to cool similar to that of apples in temperate regions. temperatures. Similar conclusions have also been Originating in the Indo-Burma region, the mango obtained by workers in Australia (Whiley 1992). has since spread to nearly all tropical areas of the Reliable flowering is necessary to obtain world. On the Indian subcontinent, it has been consistent mango production in the tropics. In under cultivation for at least 4,000 years. Hawaii, the Pacific, and the Caribbean, where land In the tropics, mangos are usually grown at is a limiting factor, mango is a potentially elevations between sea level and 1,200 meters, but important fruit crop if increased production, does best below 610 meters in climates with a reliable bearing, and off-season bearing can be pronounced dry season. In Hawaii mango will achieved. In Hawaii, biennial bearing is common; grow best at elevations from sea level to 457 a heavy crop one year may prevent further fruiting meters (1,500 ft) (Chia et a!. 1988). Mangos grow for two years or longer. In the Hawaiian Islands, best at temperatures between 27 - 24°C and are leeward sections, which are drier during the winter susceptible to frost damage.
    [Show full text]
  • Hormonal Regulation of Mango Fruit Ripening
    Department of Environment and Agriculture Hormonal Regulation of Mango Fruit Ripening Siti Zaharah Sakimin This thesis is presented for the Degree of Doctor of Philosophy of Curtin University August 2011 Declaration Declaration To the best of my knowledge and belief this thesis contains no material previously published by any other person except where due the acknowledgement has been made. This thesis contains no material which has been accepted for the award of any other degree or diploma in any university. Signature: Date: 13 th December 2011 i Dedication Dedication To: My father , Hj. Sakimin Sakidin; My mother , Hajjah Poriah Hj. Mokti; My father-in-law , Mr. Iberahim Yusuf; My mother-in-law , Mrs. Tuan Minah Tuan Kadir; My husband , Dr. Ismail Iberahim; My son , Muhammad Zahin Iqbal Ismail; & All my family members For “A constant source of inspiration and doa’a during the entire period of my PhD study and my life...” ii Acknowledgements Acknowledgements In the name of Allah, the most Beneficent and the Merciful. I bow my head, with all the humility and modesty, before Allah Almighty, the Creator of everything, Who imbibed in man a feel for knowledge and desire to explore the unknown for the benefit of mankind, for granting me courage, patience, and perseverance to undertake and accomplish this prime task of research work which could not be possible without His Blessings and Grace. May peace and Blessings of Allah Almighty be upon His all Prophets including Muhammad (PBUH), His last messenger, who is the fountain of knowledge and guidance for the salvation of mankind in this world and the hereafter.
    [Show full text]
  • INDEX to CONSIGNORS (1) Hip No
    QUARTER HORSE YEARLING INDEX TO CONSIGNORS (1) Hip No. Name of Horse Year Sex Color Sire Dam 6666 Ranch, Agent, Guthrie, TX 5 Unnamed Yearling 09 C Bay Finding Nemo I Rest My Case 66 Hooray Cowgirl 09 F Sorrel Separatist Hooray Baby 126 Unnamed Yearling 09 F Sorrel Snowbound (TB) Oh Dashaway 166 Nemos King 09 C Brown Finding Nemo Lurid 314 La Jolla Miss 09 F Bay Stoli La Jolla Ca 371 Stolis Cowboy 09 C Sorrel Stoli Special Lady Ryon 387 Luxurious Attire 09 F Sorrel Stoli Luxurious La Jolla 489 He Wants It All 09 C Brown Finding Nemo She Wants It All 504 Nemos Special 09 F Gray Finding Nemo Jetta Special 600 Sixes Strawberry 09 F Sorrel Tac It Like A Man Strawberry Cartel 768 Natovas Cowboy 09 C Brown Finding Nemo Natovas Dasher 769 Eyesa Secret 09 F Bay Eyesa Special Secretively 797 Buds Nemo 09 C Bay Finding Nemo Buds N Blossoms 802 Heza Tac 09 C Bay Heza Fast Dash Sexy Tac 831 Unnamed Yearling 09 C Sorrel Heza Fast Dash Brownsboro 839 Oceans Angel 09 F Chestnut Ocean Runaway Parading Angel 843 Sheza Nemo 09 F Bay Finding Nemo Sheza Baby 952 Stone Wagon 09 C Bay PYC Paint Your Wagon Sheza Stone Runner 981 Strawberry Kool 09 C Gray First N Kool Strawberry Cartel 983 Natovas Nemo 09 F Brown Finding Nemo Natovas Dasher 1007 Snowbound Lad 09 C Gray Snowbound (TB) La Fantastica 6666 Ranch, Agent for Burnett Ranches, LLC/M Kelly, Guthrie, TX 987 Goneforawin 09 F Bay Finding Nemo Goneforafirstdown 6666 Ranch, Agent for Burnett Ranches, LLC/ Evans & Evans, Guthrie, TX 259 Little Kool Lady 09 F Bay Shazoom Little Kool Barbie 681 Snowbound Mesa 09 C Gray Snowbound (TB) High Mesa Melody 6666 Ranch, Agent for Burnett Ranches, LLC.
    [Show full text]