Cost and Profitability Analysis of Cherry Production: The Case Study of District Quetta, Pakistan

Noor, Rana Shahzad; Hussain, Fiaz; Farooq, Muhammad Umar; Umair, Muhammad

doi:10.26480/bda.02.2020.74.80

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…According to the UN, almost 2,000 tons of cherry are produced annually in Pakistan. On a commercial basis, export-quality cherries are grown on about 897 hectares in Balochistan (mainly in Quetta, Ziarat, and Kalat), resulting in an annual production of 1,507 tonnes [3]. Similarly, potatoes (Solanum tuberosum L.) are one of the world's most extensively grown and consumed tuberous crops, and around 1300 kha of potato is planted in Pakistan [4].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

Bhatti,

Bazai,

Hussain

et al. 2023

CMC

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Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases. The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes. Through rigorous training and evaluation, the proposed system achieved an impressive accuracy rate of 99%. This mobile application serves as a convenient and valuable advisory tool, providing early detection and guidance in real agricultural environments. The significance of this research lies in its potential to revolutionize plant disease detection and management practices. By automating the identification process through deep learning algorithms, the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise. The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

Bhatti,

Bazai,

Hussain

et al. 2023

CMC

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“…Sweet cherry (Prunus avium L.) cultivation in Pakistan in the valleys and foothills of Gilgit-Baltistan viz., Gilgit, Hunza, and Nagar districts are major cherry-producing areas while in Baluchistan includes Quetta, Kalat, Pishin, Loralai, Mastung, Zhob, and Swat. The total cherry production is approximately 400 hectares in Pakistan, producing 1660 tons of cherry fruit, annually, whereas the production of cherry in GB was 2384/1302 hectares (FAOSTAT, 2019;Noor et al, 2020;Hassan et al, 2022). GB holds the country's largest share (64.77%) of cherry production.…”

Section: Introductionmentioning

confidence: 99%

Prevalence and Characteristics of Cherry Shot-Hole Disease in Gilgit-Baltistan

Ahmad,

Abbas,

Razaq

et al. 2023

Pak. J. Phytopathol.

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Shot-hole disease is one of the most common and significant diseases affecting cherry trees in Gilgit-Baltistan, Pakistan, every year. It results in small reddish areas on leaves and fruits, forming characteristic holes. However, the pathogen associated with the disease, its pathogenicity, and its growth performance under different environmental conditions remains unknown. In this study, the pathogen associated with shot-hole disease was identified as W. carpophilus. Furthermore, variations in pathogenicity were observed among the five isolates of the pathogen (SRA-1, SRA-2, SRA-3, SRA-4, and SRA-5), with SRA-5 exhibiting smaller lesion sizes and SRA-1 to SRA-4 displaying larger lesion sizes. Differential responses to temperature variations were noted, with isolates SRA-1 and SRA-4 growing faster at different temperature regimes. Conidia count and biomass measurements revealed isolate-specific patterns under varying temperature regimes. Growth performance and colony characteristics varied among isolates. Environmental responses indicated sensitivity to osmotic potential and pH, as well as preferences for different culture media. This research provides crucial insights into the morphology, behavior, and adaptation of W. carpophilus. These findings will facilitate the establishment of management strategies against shot-hole disease in cherry orchards.

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“…Other temperate zones for the growth of commercial cherry in Pakistan include Loralai, Pishin, Quetta, Mastung, Kalat, Zhob, and Swat. Recently, cherry has been cultivated in Baluchistan on about 897 hectares, with an annual production of about 1507 tons [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

First Report of Fruit Rot of Cherry and Its Control Using Fe2O3 Nanoparticles Synthesized in Calotropis procera

et al. 2022

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Cherry is a fleshy drupe, and it is grown in temperate regions of the world. It is perishable, and several biotic and abiotic factors affect its yield. During April–May 2021, a severe fruit rot of cherry was observed in Swat and adjacent areas. Diseased fruit samples were collected, and the disease-causing pathogen was isolated on PDA. Subsequent morphological, microscopic, and molecular analyses identified the isolated pathogen as Aspergillus flavus. For the control of the fruit rot disease of cherry, iron oxide nanoparticles (Fe2O3 NPs) were synthesized in the leaf extract of Calotropis procera and characterized. Fourier transform infrared (FTIR) spectroscopy of synthesized Fe2O3 NPs showed the presence of capping and stabilizing agents such as alcohols, aldehydes, and halo compounds. X-ray diffraction (XRD) analysis verified the form and size (32 nm) of Fe2O3 NPs. Scanning electron microscopy (SEM) revealed the spinal-shaped morphology of synthesized Fe2O3 NPs while X-ray diffraction (EDX) analysis displayed the occurrence of main elements in the samples. After successful preparation and characterization of NPs, their antifungal activity against A. flavus was determined by poison technique. Based on in vitro and in vivo antifungal activity analyses, it was observed that 1.0 mg/mL concentration of Fe2O3 can effectively inhibit the growth of fungal mycelia and decrease the incidence of fruit rot of cherry. The results confirmed ecofriendly fungicidal role of Fe2O3 and suggested that their large-scale application in the field to replace toxic chemical fungicides.

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Cost and Profitability Analysis of Cherry Production: The Case Study of District Quetta, Pakistan

Cited by 5 publications

References 7 publications

Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

Prevalence and Characteristics of Cherry Shot-Hole Disease in Gilgit-Baltistan

First Report of Fruit Rot of Cherry and Its Control Using Fe2O3 Nanoparticles Synthesized in Calotropis procera

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