• International Journal of Industrial Entomology and Biomaterials
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• v.6 no.2
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• pp.139-143
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• 2003

Mathematical constants for multiplication with leaf length (I) or breadth (b) or l ${\times}$ b have been worked out for determining leaf area in promising mulberry genotypes viz., Chinese White, S-146, Chak Majra and Sujanpur Local of sub-tropical India. When pooled, the mathematical constants worked out were 8.1132, 10.1019 and 0.5992 for multiplication with leaf length, breadth and l ${\times}$ b, respectively, for genotypes bearing un-lobbed leaves and 6.9447, 8.2761 and 0.5009 for multiplication with leaf length, breadth and l ${\times}$ b, respectively for genotypes bearing lobbed leaves. Leaf area can be worked out by using any constant by multiplying either with leaf length or breadth or both (l ${\times}$ b). Estimated leaf areas worked out were found significantly and positively correlated with actual leaf area (r＝999$^{＊＊}$). The suggested present non-destructive method by using mathematical constants is very quick and alternative to electronic leaf area meter for spot leaf area determination in mulberry which is the only food source for mulberry silkworm in sericulture industry.

• Journal of Sericultural and Entomological Science
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• v.8
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• pp.11-25
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• 1968

Various formulae for estimation of leaf production in mulberry trees were investigated and obtained. Four varieties of mulberry trees were used as the materials, and seven characters namely branch length. branch diameter, node number per branch, total branch weight, branch weight except leaves, leaf weight and leaf area, were studied. The formulae to estimate the leaf yield of mulberry trees are as follows: 1. Varietal differences were appeared in means, variances, standard devitations and standard errors of seven characters studied as shown in table 1. 2. Y$_1$=a$_1$X$_1$${\times}$P$_1$......(l) where Y$_1$ means yield per l0a by branch number and leaf weight determination. a$_1$.........leaf weight per branch. X$_1$.......branch number per plant. P$_1$........plant number per l0a. 3. Y$_2$=(a$_2$${\pm}$S. E.${\times}$X$_2$)+P$_1$.......(2) where Y$_2$ means leaf yield per l0a by branch length and leaf weight determination. a$_2$......leaf weight per meter of branch length. S. E. ......standard error. X$_2$....total branch length per plant. P$_1$........plant number per l0a as written above. 4. Y$_3$=(a$_3$${\pm}$S. E${\times}$X$_3$)${\times}$P$_1$.....(3) where Y$_3$ means of yield per l0a by branch diameter measurement. a$_3$.......leaf weight per 1cm of branch diameter. X$_3$......total branch diameter per plant. 5. Y$_4$=(a$_4$${\pm}$S. E.${\times}$X$_4$)P$_1$......(4) where Y$_4$ means leaf yield per 10a by node number determination. a$_4$.......leaf weight per node X$_4$.....total node number per plant. 6. Y$\sub$5/= {(a$\sub$5/${\pm}$S. E.${\times}$X$_2$)Kv}${\times}$P$_1$.......(5) where Y$\sub$5/ means leaf yield per l0a by branch length and leaf area measurement. a$\sub$5/......leaf area per 1 meter of branch length. K$\sub$v/......leaf weight per 100$\textrm{cm}^2$ of leaf area. 7. Y$\sub$6/={(X$_2$$\div$a$\sub$6/${\pm}$S. E.)}${\times}$K$\sub$v/${\times}$P$_1$......(6) where Y$\sub$6/ means leaf yield estimated by leaf area and branch length measurement. a$\sub$6/......branch length per l00$\textrm{cm}^2$ of leaf area. X$_2$, K$\sub$v/ and P$_1$ are written above. 8. Y$\sub$7/= {(a$\sub$7/${\pm}$S. E. ${\times}$X$_3$)}${\times}$K$\sub$v/${\times}$P$_1$.......(7) where Y$\sub$7/ means leaf yield estimates by branch diameter and leaf area measurement. a$\sub$7/......leaf area per lcm of branch diameter. X$_3$, K$\sub$v/ and P$_1$ are written above. 9. Y$\sub$8/= {(X$_3$$\div$a$\sub$8/${\pm}$S. E.)}${\times}$K$\sub$v/${\times}$P$_1$.......(8) where Y$\sub$8/ means leaf yield estimates by leaf area branch diameter. a$\sub$8/......branch diameter per l00$\textrm{cm}^2$ of leaf area. X$_3$, K$\sub$v/, P$_1$ are written above. 10. Y$\sub$9/= {(a$\sub$9/${\pm}$S. E.${\times}$X$_4$)${\times}$K$\sub$v/}${\times}$P$_1$......(9) where Y$\sub$7/ means leaf yield estimates by node number and leaf measurement. a$\sub$9/......leaf area per node of branch. X$_4$, K$\sub$v/, P$_1$ are written above. 11. Y$\sub$10/= {(X$_4$$\div$a$\sub$10/$\div$S. E.)${\times}$K$\sub$v/}${\times}$P$_1$.......(10) where Y$\sub$10/ means leaf yield estimates by leaf area and node number determination. a$\sub$10/.....node number per l00$\textrm{cm}^2$ of leaf area. X$_4$, K$\sub$v/, P$_1$ are written above. Among many estimation methods. estimation method by the branch is the better than the methods by the measurement of node number and branch diameter. Estimation method, by branch length and leaf area determination, by formulae (6), could be the best method to determine the leaf yield of mulberry trees without destroying the leaves and without weighting the leaves of mulberry trees.

• Journal of Bio-Environment Control
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• v.31 no.2
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• pp.98-103
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• 2022

Non-destructive estimation of leaf area is a more efficient and convenient method than leaf excision. Thus, several models predicting leaf area have been developed for various horticultural crops. However, there are limited studies on estimating the leaf area of strawberry plants. In this study, we predicted the leaf areas via nonlinear regression analysis using the leaf lengths and widths of three-compound leaves in five domestic strawberry cultivars ('Arihyang', 'Jukhyang', 'Keumsil', 'Maehyang', and 'Seollhyang'). The coefficient of determination (R²) between the actual and estimated leaf areas varied from 0.923 to 0.973. The R² value varied for each cultivar; thus, leaf area estimation models must be developed for each cultivar. The leaf areas of the three cultivars 'Jukhyang', 'Seolhyang', and 'Maehyang' could be non-destructively predicted using the model developed in this study, as they had R² values over 0.96. The cultivars 'Arihyang' and 'Geumsil' had slightly low R² values, 0.938 and 0.923, respectively. The leaf area estimation model for each cultivar was coded in Python and is provided in this manuscript. The estimation models developed in this study could be used extensively in other strawberry-related studies.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.325-331
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• 2022

One of the most important factors in predicting tomato growth and yield is the leaf area. Estimating leaf area accurately is the beginning of an effective tomato plant growth assessment model. To this end, this study was conducted to identify the most effective model for estimating plant leaf area through the measurement of tomato plant leaves. Leaf area (LA), leaf length (L), leaf width (W), and lamina length (La) were measured for all leaves of 5 plants at two-week intervals. The correlation between LA and tomato-leaf-independent variables showed a strong positive relationship with the formulas La × W, L × W, La + W, and L + W. For LA estimation, a linear model using the formula LA = a + b (La² + W²) gave the most accurate estimation (R² = 0.867, RMSE = 88.76). After examining the positions of upper, middle, and lower leaves from September to December, the coefficient of determination (R²) values for each model were 0.878, 0.726, and 0.794 respectively. The most accurate estimation came from the model that used the upper leaves of the plants. The high accuracy of the upper-leaf-based model is judged by the 50% defoliation performed by farmers after October.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.6
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• pp.560-567
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• 1991

Photosynthetic variation in field grown soybean [Glycine max (L.) Merr. cv Hodgson78］ was studied in relation to leaf anatomical variation. Variations in mesophyll morphology were accentuated by manipulating source and sink size. At R3 stage, two treatments were started: one was thinning and continu-ous debranching(6. 5 plants rather than 26 plants per m of row and remaining plants were debranched weekly), and the other was continuous partial depodding (allowing only one pod to develop at each mainstem node). Gas exchange characteristics, mesophyll cell volume and surface area per unit leaf surface, and microclimatic parameters were measured on the intact terminal leaflet at the 10th node. Observations were made 5 times with 3 to 4 day intervals starting R4 stage. Two models were used to compute leaf photosynthetic rates: one considered no effect of mesophyll morphology on photosynthesis, and the other considered potential effects of variations in mesophyll cell volume and surface area on diffusion and biochemical processes. Seventy nine percent of total photosynthetic variations observed in the experiment was explained by the latter, while 69% of the same variations was explained by the former model. By incorporating the mesophyll morphology concept, the predictability was improved by 14.6% in the field condition. Additional Index Words: photosynthesis model, leaf anatomy, Glycine max (L.) Merr., mesophyll surface area, mesophyll cell volume.

• Journal of Life Science
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• v.8 no.5
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• pp.492-496
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• 1998

Intact measurement of leaf water content was attempted using the transmission of beta radiation. Prior to the experiments, two tested plants, rice and soybean, were grown in 1/5000a wagner pot. The moisure ratio of plant leaves were measured with the beta radiation transmission method using a G-M detector and $^{99}$ Tc as the beta ray sou-rce. Beta radiation transmission showed a tendancy to increase with the passage of time after leaf cutting. However, it showed a tendancy to decrease with water supply for the lack of water. A positive correlation was found between the leaf water content and beta radiation transmission. The mutiple regression analysis about leaf water content was obtained that the coefficient of partial regression for beta radiation transmission was much higher(rice; -0.863, soybean; -0.904) than that for specific leaf area(rice; 0.007, soybean: 0.004).

• Journal of Biosystems Engineering
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• v.40 no.1
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• pp.89-93
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• 2015

Purpose: Machine vision-based image processing methods can be useful for estimating the fresh weight of plants. This study analyzes the ability of two different image processing methods, i.e., morphological and pixel-value analysis methods, to measure the fresh weight of lettuce grown in a closed hydroponic system. Methods: Polynomial calibration models are developed to relate the number of pixels in images of leaf areas determined by the image processing methods to actual fresh weights of lettuce measured with a digital scale. The study analyzes the ability of the machine vision- based calibration models to predict the fresh weights of lettuce. Results: The coefficients of determination (> 0.93) and standard error of prediction (SEP) values (< 5 g) generated by the two developed models imply that the image processing methods could accurately estimate the fresh weight of each lettuce plant during its growing stage. Conclusions: The results demonstrate that the growing status of a lettuce plant can be estimated using leaf images and regression equations. This shows that a machine vision system installed on a plant growing bed can potentially be used to determine optimal harvest timings for efficient plant growth management.

• Journal of Korean Society of Forest Science
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• v.70 no.1
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• pp.103-108
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• 1985

This paper is concerned with estimating equations of leaf area(A) obtained from linear measurements - leaf length(L) and leaf width(W) - on the leaves of 13 species composing a natural mixed stand in Mt. Jiri. This method is known to be rapid and non-destructive in estimating leaf area. The equation of A=bLW is frequently used in rough and rapid estimation. Each species in this study has its own coefficient b according to its geometrical leaf shape. The range of coefficients of 13 species was 0.579 to 0.717. This means that the relationship A=2/3LW is suitable to most broad leaf species in a natural mixed stand in Mt. Jiri. When more precise estimation of leaf area is needed, full regression equation is used. In this study, the form of ${\log}A=b_0+b_1{\log}LW$ was the most precise estimation equation in 8 species. In addition to this, the form of $A=b_0+b_1LW$ and $A=b_0+b_1L^2+b_2W^2$ were founded to be suitable for estimation of leaf area. In comparision of these two forms, the determination coefficient were about the same, but the F-value of the former was greater than that of the latter. Therefore, the use of the former seems to be more reliable and practical.

• The Korean Journal of Pesticide Science
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• v.19 no.2
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• pp.113-118
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• 2015

This study was carried out to develop the economic thresholds for powdery mildew on pepper. To investigate the relationship between powdery mildew incidence degree and yield, experimental plots with ten treatments as initial disease degree were established. Disease intensity exhibited negative and significant correlation with fruit characters like fruit length, fruit diameter, fruit weight. The adverse effect of the disease on these characteristics was low yield, exhibiting significant negative correlation with disease intensity. There existed close correlation between rate of infected leaf area and yields in the plastic house (Chonhatongil: Y = -3.44X+291.09 $R^2=0.73$, Buchon: Y = -2.14X+327.9 $R^2=0.78$). There existed close correlation between rate of infected leaf area and yield loss in the plastic house (Chonhatongil: Y = 2.14X+15.45 $R^2=0.76$ $r=0.87^{**}$, Buchon: Y = 3.44X+114.21 $R^2=0.73$ $r=0.85^{**}$). Control thresholds diseased rate on powdery mildew of pepper was below 3.2 to 7.3% rate of infected leaf area per plant in the plastic house. The economic thresholds for powdery mildew of pepper was below 3.8 to 6.2% rate of infected leaf area per plant in the plastic house.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.