Apples (Malus domestica, Borkh.) Phenology in Ethiopian Highlands: Plant Growth, Blooming, Fruit Development and Fruit Quality Perspectives

Melke, Abayneh; Fetene, Masresha

doi:10.9734/ajea/2014/9783

Cited by 8 publications

(6 citation statements)

References 82 publications

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“…The size, soluble solids content, and titratable acid content of fruit play important roles in the external quality and edible flavor of fruit (Melke & Fetene, 2014), and the mineral elements, especially in soil and leaves, have important influences on fruit growth, fruit formation, and fruit quality control (Aular et al., 2017; Nestby et al., 2005). Soil is an important factor for material and energy exchange in the ecosystem, soil nutrients elements are closely related to the growth, development of fruit trees yields, and fruit quality and leaves are the main organs that plants use for photosynthesis, production of nutrients, and directly supply mineral nutrition for tree growth and fruit (Velemis et al., 1999).…”

Section: Discussionmentioning

confidence: 99%

Using artificial neural network in predicting the key fruit quality of loquat

Huang

Wang²,

et al. 2021

Food Science & Nutrition

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The formation and regulation of loquat fruit quality have always been an important research field to improve fruit quality, commodities, and market value. Fruit size, soluble solids content, and titratable acid content represent the most important quality factors in loquat. Mineral nutrients in abundance or deficiency are among the most important key factor that affect fruit quality. In the present study, we use artificial neural network (ANN) to explore the effects of mineral nutrients in soil and leaves on the key fruit quality of loquat. The results show that the ANN model with the structure of 12–12–1 can predict the single fruit weight with the highest accuracy (R2 = .91), the ANN model with the structure of 10–11–1 can predict the soluble solid content with the highest accuracy (R2 = .91), and the ANN model with the structure of 9–10–1 can predict the titratable acid content with the highest accuracy (R2 = .95). Meanwhile, we also conduct sensitivity analysis to analyze the relative contribution of mineral nutrients in soils and leaves to determine of the key fruit quality. In terms of relative contribution, Ca, Fe, and Mg content in soils and Zn, K, and Ca content in leaves contribute relatively largely to a single fruit weight, Mn, Fe, and Mg content in soils and the N content in leaves contribute relatively largely to the soluble solid content, and the P, Ca, N, Mg, and Fe in leaves contribute relatively largely to the titratable acid content of loquat. The established artificial neural network prediction models can improve the quality of loquat fruit by optimizing the content of mineral elements in soils and leaves.

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Section: Discussionmentioning

confidence: 99%

Using artificial neural network in predicting the key fruit quality of loquat

Huang

Wang²,

et al. 2021

Food Science & Nutrition

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“…ALS mainly infects tender leaves and spring shoots of the apple tree leaves at the leaf sprouting and spreading stages [23,24]. The fruit bagging, pruning, pesticide spraying, and other disease and pest control practices are usually conducted at fruit expansion stage to promote the growth of apple trees and development of fruits [25,26].…”

Section: Feature Selection Of Meteorological Datamentioning

confidence: 99%

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Huang

Zhang

et al. 2022

Agronomy

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Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases’ and pests’ forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices.

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“…It should be pointed out that the formation of apple quality is a complex, systematic process. It is affected by not only meteorological factors (light, temperature, temperature difference between day and night, and relative humidity) but also nonclimatic factors (soil types, fertility levels, and cultivation techniques), and even the interactions among these factors (Melke and Fetene, 2014;Warrington et al, 1999). This study only focused on the effect of major meteorological factors on apple fruit quality.…”

Section: Restrictive Meteorological Factors and Countermeasures For Hmentioning

confidence: 99%

“…The Circum-Bohai and Loess Plateau production regions are the two dom-inant apple production regions, with cultivation area and yield accounting for more than 80% of the total in China. Fruit development and quality are mainly affected by meteoro-logical factors, soil nutrients, and cultivation techniques (Mattheis and Fellman, 1999), with meteorological factors playing a key role in fruit ripening and quality formation (Melke and Fetene, 2014). Due to regional differences in climatic conditions and cultivation techniques, 'Fuji' apples in the two dominant production regions differ greatly in fruit size, appearance, color, and taste (Feng et al, 2013;Ma et al, 2015;Wang et al, 2013;Zhang et al, 2017).…”

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confidence: 99%

Analysis and Optimization of the Effects of Meteorological Factors on ‘Fuji’ Fruit Quality in Two Dominant Production Regions of China

Zhang¹,

Li²,

Zhou³

et al. 2021

horts

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This study aimed to understand the effects of meteorological factors on the ‘Fuji’ apple quality in the Circum-Bohai and Loess Plateau apple production regions of China and to guide apple production based on local climate. Fruit samples of the ‘Fuji’ apple and meteorological data were investigated from 132 commercial ‘Fuji’ apple orchards covering 44 counties in the two aforementioned production regions (22 counties per region). The partial least-squares regression (PLSR) method was first used to screen major meteorological factors that greatly affected fruit quality; these were subsequently used to establish the regression equation of fruit quality attributes and major meteorological factors. Linear programming was used to estimate optimum meteorological factors for good apple quality. The results showed that in the Circum-Bohai production region, many meteorological factors (total annual precipitation, total precipitation from April to October, lowest temperature from April to October, sunshine percentage from April to October) were significantly higher than those in the Loess Plateau production region; however, the temperature difference between day and night from April to October was significantly smaller than that in the Loess Plateau production region. The soluble solids content and skin color area of apples from the Loess Plateau production region were significantly greater than those from the Circum-Bohai production region. The same fruit quality factor of ‘Fuji’ apple was affected by different meteorological factors in the two production regions. The monthly mean temperature and monthly highest temperature from April to October of the Circum-Bohai production region had relatively larger positive effect weights on fruit quality, whereas the total annual precipitation, monthly mean relative humidity from April to October, and total precipitation from April to October of the Loess Plateau production region had relatively larger positive effect weights on fruit quality. The major influencing meteorological factors of the fruit soluble solids content were total precipitation from April to October (X7), mean annual temperature (X1), and the monthly highest temperature from April to October (X5) in the Circum-Bohai production region; however, it included the monthly mean temperature difference between day and night from April to October (X6), total annual precipitation (X2), and total precipitation from April to October (X7) in the Loess Plateau production region. In the Circum-Bohai production region, the optimum meteorological factors for ‘Fuji’ fruit quality of vigorous apple orchards were the mean annual temperature (13.4 °C), total annual precipitation (981 mm), monthly mean temperature (16.8 to 22.4 °C), lowest temperature (11.9 °C), highest temperature (19.5 to 26.8 °C), temperature difference between day and night (12.3 °C), total precipitation (336–793 mm), relative humidity (55.7% to 70.7%), and sunshine percentage (42.3% to 46.1%) during the growing period (April–October). In the Loess Plateau production region, the optimum meteorological factors for ‘Fuji’ fruit quality of vigorous apple orchards were the mean annual temperature (5.5 to 11.6 °C), total annual precipitation (714 mm), monthly mean temperature (13.3 to 19.9 °C), lowest temperature (7.9 to 9.3 °C), highest temperature (19.6 to 27.3 °C), temperature difference between day and night (7.1 to 12.4 °C), total precipitation (338–511 mm), relative humidity (56.1% to 82.4%), and sunshine percentage (37.3% to 55.9%) during the growing period (April–October). The restrictive factors for high-quality ‘Fuji’ apples of the Circum-Bohai production region were the smaller monthly mean temperature difference between day and night, higher monthly mean lowest temperature, and larger monthly mean relative humidity during the growing period; however, those of the Loess Plateau production region were drought or less precipitation from November to March, lower monthly mean temperature, and higher monthly mean highest temperature during the growing period.

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Apples (Malus domestica, Borkh.) Phenology in Ethiopian Highlands: Plant Growth, Blooming, Fruit Development and Fruit Quality Perspectives

Cited by 8 publications

References 82 publications

Using artificial neural network in predicting the key fruit quality of loquat

Using artificial neural network in predicting the key fruit quality of loquat

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Analysis and Optimization of the Effects of Meteorological Factors on ‘Fuji’ Fruit Quality in Two Dominant Production Regions of China

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