Estimating Leaf Area Research Articles

Model for estimating red mulberry leaf area using a genetic algorithm

The success of sericulture depends directly on the quality and quantity of mulberry leaves, as it is essential for the feeding and development of silkworm caterpillars and, consequently, influences the quality of the silk thread manufactured. The estimation of mulberry leaf area is important to have plant development and growth indicators, such as transpiration intensity, net assimilation rate, leaf area ratio, specific leaf area and leaf area index, which allow predicting crop productivity. Thus, the objective of this study was to develop and test a model capable of estimating the red mulberry leaf area using a genetic algorithm. The model was adjusted with the proposed stochastic optimization method. The mean error found for the tested dataset was approximately 228.17 mm2 in sample space with mean leaf area of 6515.55 mm2. The information generated allows applying the model to estimate red mulberry leaf area in future studies.

Morphology and Allometry of Juvenile Açaí Palms Under Cultivation Conditions in Central Amazonia

Two Amazonian species of açaí palm trees (Euterpe oleracea and Euterpe precatoria) are exploited in the commercial production of açaí pulp or juice. While E. oleracea benefits from developed cultivation technologies, E. precatoria lacks such advancements. Studies on the morphology and development of açaí palms under cultivation conditions can contribute to increasing the productivity of the species. The aim of this study was to carry out morphological characterization, assess growth and development in the juvenile phase of the plants, and obtain allometric models for E. precatoria and E. oleracea. Evaluations were conducted between 44 and 48 months post-planting. Allometric equations were formulated to accurately estimate leaf area. The results showed that E. oleracea begins reproduction earlier and exhibits greater growth in stem dimensions and leaf areas compared to E. precatoria, indicating that E. precatoria can be cultivated at higher planting densities. Allometric models, based on leaf length and width, effectively predicted individual leaf areas for both species, demonstrating their utility in optimizing cultivation strategies.

Photosynthetic temperature responses in leaves and canopies: why temperature optima may disagree at different scales.

Understanding how canopy-scale photosynthesis responds to temperature is of paramount importance for realistic prediction of the likely impact of climate change on forest growth. The effects of temperature on leaf-scale photosynthesis have been extensively documented but data demonstrating the temperature response of canopy-scale photosynthesis are relatively rare, and the mechanisms that determine the response are not well quantified. Here, we compared leaf- and canopy-scale photosynthesis responses to temperature measured in a whole-tree chamber experiment and tested mechanisms that could explain the difference between leaf and crown scale temperature optima for photosynthesis. We hypothesised that 1) there is a large contribution of non-light saturated leaves to total crown photosynthesis; 2) photosynthetic component processes vary vertically through the canopy following the gradient in incident light; and 3) seasonal temperature acclimation of photosynthetic biochemistry has a significant role in determining the overall temperature response of canopy photosynthesis. We tested these hypotheses using three models of canopy radiation interception and photosynthesis parameterized with leaf-level physiological data and estimates of canopy leaf area. Our results identified the influence of non-light saturated leaves as a key determinant of the lower temperature optimum of canopy photosynthesis, which reduced the temperature optimum of canopy photosynthesis by 6-8°C compared to the leaf scale. Further, we demonstrate the importance of accounting for within-canopy variation and seasonal temperature acclimation of photosynthetic biochemistry in determining the magnitude of canopy photosynthesis. Overall, our study identifies key processes that need to be incorporated in terrestrial biosphere models to accurately predict temperature responses of whole-tree photosynthesis.

Comparative analysis of leaf area index and maize yield estimation assimilating remote sensing and DSSAT crop simulation model

Maize is a global staple crop, impacting food security, economic development, and agricultural sustainability. This study investigates the integration of Sentinel-1A Synthetic Aperture Radar (SAR) data with the DSSAT CERES-Maize crop simulation model to estimate Leaf Area Index (LAI) and rabi maize yield in Belagavi district, Karnataka. Field data, including LAI, days to anthesis, silking, grain filling, and farmers' field practices, were collected for model calibration and validation, supplemented by crop-cutting experiments (CCE) to determine actual yields. The study revealed strong correlations between LAI values obtained from remote sensing (RS) and field observations, with RS-derived LAI showing an average agreement of approximately 96.07% compared to field measurements. The DSSAT model exhibited slightly better performance, averaging 97.09%. Statistical analysis for LAI showed an R² value of 0.853 for RS and 0.864 for DSSAT, indicating strong correlations with observed LAI values. For maize yield estimation, the DSSAT model demonstrated higher accuracy with an average yield of 8129 kg/ha, compared to RS-derived yield averages of 7533.9 kg/ha and CCE yield averages of 8096.6 kg/ha. The average concordance between DSSAT and CCE yields was 94.19%, while RS and CCE yields had an average concordance of 92.29%. Statistical analyses revealed coefficients of determination of 0.854 for DSSAT-CCE and 0.867 for RS-CCE comparisons. The study underscores the value of combining RS data with DSSAT for comprehensive and accurate crop yield forecasting, highlighting the potential for improved agricultural assessments and decision-making.

Leaf growth pattern and morphology of Sauropus androgynus (L.) Merr. in tropical lowland

Chikurmanis or katuk (Sauropus androgynus (L.) Merr.) is a traditional leafy vegetable that is consumed widely in Indonesia. The leaf is rich in vitamins, minerals, nutrients, and dietary fiber. The study aimed to investigate the leaf growth pattern and morphological characteristics of the chikurmanis cultivated in the tropical lowland ecosystem. The research was conducted on local farmland in Marga Sakti Sebelat, Bengkulu, Indonesia on December 2023 by survey method. The results showed that the leaf of chikurmanis grew in the daytime and continued to grow at nighttime as indicated in midrib length, leaf width, and foliole number. It is likely that nighttime growth become predominant in chikurmanis. The plant was a type of shrub with complete organs of stem, branch, root, flower, and leaf. The leaf was arranged as a compound leaf with many folioles in opposite positions. Midrib length (M) x leaf width (W) with a zero intercept of linear regression was the most reliable predictor of leaf area with the formula Y = 0.4964MxW (R2 = 0.9677). Keywords: chikurmanis plant; katuk; leaf area estimation; morphological trait; traditional leafy vegetable

Estimating Leaf Area Index in Apple Orchard by UAV Multispectral Images with Spectral and Texture Information

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R2, root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management.

The Selection of Lettuce Seedlings for Transplanting in a Plant Factory by a Non-Destructive Estimation of Leaf Area and Fresh Weight

Selecting uniform and healthy seedlings is important to ensure that a certain level of production can be reliably achieved in a plant factory. The objectives of this study were to investigate the potential of non-destructive image analysis for predicting the leaf area and shoot fresh weight of lettuce and to determine the feasibility of using a simple image analysis to select robust seedlings that can produce a uniform and dependable yield of lettuce in a plant factory. To vary the range of the leaf area and shoot fresh weight of lettuce seedlings, we applied two- and three-day irrigation intervals during the period of seedling production and calculated the projected canopy size (PCS) from the top-view images of the lettuce seedlings, although there were no significant growth differences between the irrigation regimes. A high correlation was identified between the PCS and shoot fresh weight for the lettuce seedlings during the period of seedling production, with a coefficient of determination exceeding 0.8. Therefore, the lettuce seedlings were classified into four grades (A–D) based on their PCS values calculated at transplanting. In the early stages of cultivation after transplanting, there were differences in the lettuce growth among the four grades; however, at the harvest (28 days after transplanting), there was no significant difference in the lettuce yield between grades A–C, with the exception of grade D. The lettuce seedlings in grades A–C exhibited the anticipated yield (150 g/plant) at the harvest time. In the correlation between the PCS and leaf area or the shoot fresh weight of lettuce during the cultivation period after transplanting and the entire cultivation period, the R2 values were higher than 0.9, confirming that PCS can be used to predict lettuce growth with greater accuracy. In conclusion, we demonstrated that the PCS calculation from the top-view images, a straightforward image analysis technique, can be employed to non-destructively and accurately predict lettuce leaf area and shoot fresh weight, and the seedlings with the potential to yield above a certain level after transplanting can be objectively and accurately selected based on PCS.

Specific leaf area and its within-individual variation in understory evergreen and deciduous woody species in New Zealand

Specific leaf area (SLA) plays a critical role in carbon assimilation and nutrient cycling. While leaf habit (deciduous vs. evergreen) has often been recognized as a reliable predictor of SLA—with deciduous species typically having higher mean SLA values due to lower concentration of structural components compared to evergreens—high variation in SLA among evergreen species suggests further investigation of variation for species with this leaf habit could improve predictions of SLA effects on community and ecosystem processes. Furthermore, the presence of leaves of different ages in evergreen plants, emerging over multiple years, could amplify the within-individual variation in SLA, which remains underexplored. Here we report variations of SLA measured from c. 2000 leaves collected from 36 individuals across 19 woody species in an understory environment in New Zealand (NZ). We found that while most deciduous species, predominantly non-native, clustered towards higher SLA values, evergreen species presented a wide SLA spectrum. Moreover, we found that while being deciduous, having a smaller leaf size, and younger leaves, collected from lateral branches, correlated with elevated SLA values, the leaf habit did not primarily drive the within-individual SLA variation. Instead, smaller leaf size emerged as a significant predictor of within-individual SLA variation. The branch-order effect on SLA underscores a methodological consideration: accurate estimation of total leaf area in evergreen trees requires representative sampling across all branch orders. Our study also highlights the need for integrating leaf traits such as leaf size and branch order into functional trait analyses. Further research is vital to understand the underlying mechanisms of these trait variations and their impacts on ecosystem functioning.

Leaf area estimation based on ANFIS using embedded system and PV panel

Leaf area is one of the important parameters for plant canopy development. It is used as an indicator closely related to plant growth in several studies on plant production. However, most leaf area meters used today are costly and rely on human observations. This situation may be limiting for researchers in terms of having proper leaf area measuring devices. The reliance on human-focused measurements leads to human errors. Digital scanners and cameras, digital image processing-based estimation methods, paper weighing, grid counting, regression equations, width and height correlation models, planimeters, laser optics, and handheld scanners can be used to determine leaf area. However, some of these methods are expensive and unnecessary for simple studies. Therefore, this study aims to design and implement an embedded system with a simpler, cheaper alternative to the currently used methods and devices, minimizing human errors. The proposed embedded system serves as a tool for measuring leaf area using a photovoltaic panel (PV) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the study, geometric shapes with known areas are used as the learning data, and real plant leaves with known areas are used in the testing process. As a result, the prediction made by ANFIS is observed to have an accuracy of R2 = 0.99.

Allometric modelling to estimate the leaf area of purslane (Portulaca umbraticola Kunth.)

Portulaca umbraticola is a species that has several uses, including ornamental. Among the variables measured in ornamental plants are the biometric data of the leaves, which can be obtained through image analysis, using indirect method, which is simple, accurate and economically viable. This work aimed to propose equations obtained by mathematical models to estimate the leaf area of P. umbraticola. The study was conducted at the Universidade Federal Rural do Semi-Árido, Mossoró, RN, Brazil. Three hundred leaves of each of the five genotypes of P. umbraticola were evaluated for length, width, product between length and width, and leaf area. Determination and correlation coefficients, Akaike information criterion, Willmott agreement index, coefficient of variation, normality using the Shapiro-Wilk test, principal components and Student's t-test were used to systematize the data. Descriptive measurements obtained from the leaf blades of P. umbraticola showed variability between genotypes, demonstrating that the sample was representative for the population analyzed. The regression models obtained were linear and potential. Allometric equations can be used to estimate leaf area in P. umbraticola, and the recommended equations are ŷ = 0.356*L (R² = 0.91) for length, ŷ = 1.665*W (R² = 0.90) for width and ŷ = 0.016 × 0.0711*LW (R² = 0.99), ŷ = 0.715*LW (R² = 0.99) and ŷ = 0.724*LW (R² = 0.99) for the relationship between leaf length and width. The equations to estimate the leaf area of P. umbraticola showed excellent data fits, with values of R² = 0.993 and little dispersion of the data.

Estimating leaf area index of Eucalyptus dunnii over the dry and wet seasons using vegetation indices and image texture derived from WorldView-3 imagery

ABSTRACT Leaf Area Index (LAI) remains one of the most important forest structural attributes, as accurate estimation of LAI is crucial for predicting the growth of different species. Using the Partial Least Squares Regression (PLS-R) algorithm, this study investigated three image processing techniques to determine the best technique for estimating LAI using WorldView-3 imagery in the Midlands, KwaZulu-Natal province of South Africa. The PLS-R texture ratios model achieved the highest accuracy of R 2 = 0.70, RMSE = 1.21 (2.32% of the mean measured LAI) and R 2 = 0.72, RMSE = 1.26 (2.41% of the mean measured LAI) for the wet and dry seasons, respectively. This was followed by the PLS-R single texture band model that produced an accuracy of R 2 = 0.65, RMSE = 1.35 (2.58% of the mean measured LAI) and R 2 = 0.67, RMSE = 1.32 (2.52% of the mean measured LAI) for the wet and dry seasons, respectively. The PLS-R model using a combination of vegetation indices had the lowest estimation accuracy of R 2 = 0.59, RMSE = 1.38 (2.64% of the mean measured LAI) and R 2 = 0.60, RMSE = 1.40 (2.67% of the mean measured LAI) for the wet and dry seasons, respectively. The results of this study provided evidence that image texture ratios can be used to estimate LAI effectively.

Comparison between computer recognition and manual measurement methods for the estimation of leaf area.

Leaf area (A) is a crucial indicator of the photosynthetic capacity of plants. The Montgomery equation (ME), which hypothesizes that A is proportional to the product of leaf length (L) and width (W), is a valid tool for non-destructively measuring A for many broadleaved plants. At present, the methods used to compute L and W for the ME can be broadly divided into two kinds: using computer recognition and measuring manually. However, the potential difference in the prediction accuracy using either method has not been thoroughly examined in previous studies. In the present study, we measured 540 Alangium chinense leaves, 489 Liquidambar formosana leaves and 215 Liriodendron × sinoamericanum leaves, utilizing computer recognition and manual measurement methods to determine L and W. The ME was used to fit the data determined by the two methods, and the goodness of fits were compared. The prediction errors of A were analysed by examining the correlations with two leaf symmetry indices (areal ratio of the left side to the right side, and standardized index for bilateral asymmetry), as well as the leaf shape complexity index (the leaf dissection index). The results indicate that there is a neglectable difference in the estimation of A between the two methods. This further validates that the ME is an effective method for estimating A in broadleaved tree species, including those with lobes. Additionally, leaf shape complexity significantly influenced the estimation of A. These results show that the use of computer recognition and manual measurement in the field are both effective and feasible, although the influence of leaf shape complexity should be considered when applying the ME to estimate A in the future.

Comparative Analysis of Impact of Soil Mixture and Fertilization on Growth and Seedling Quality of Selected Agroforestry Tree Species

The study was conducted to determine the most suitable soil amendment for germination and early seedling growth of selected indigenous multipurpose agroforestry tree species, viz., Cordia africana, Fiadherbia albida, Millettia ferruginea, and Moringa stenopetala at Haramaya University, Ethiopia. Seedlings were raised in polythene tubes having 10 cm diameter and 15 cm height and the experimental plots were laid out with eight treatments (i.e., potting soil mixture of, A = agricultural soil, M = manure, S = sand, D = DAP, and U = Urea) and three replications. The morphological attributes of seedlings such as shoot and root height, root collar diameter, leaf area were measured to assess tree seedlings vigor and robustness. Analysis of variance indicated that the shoot height of C. africana, F. albida, and M. ferruginea were significantly different (P<0.05) whereas shoot height of M. stenopetala was highly significant (P<0.01). Actual leaf area (cm2) of F. albida and M. stenopetala were significantly different (P<0.05) and estimated leaf area (cm2) of the four species were highly significant at (P<0.01). Treatment 8 (3A: 2M: 1S) for C. africana, treatment 5 (3A: 2M: 1S: 23U) for F. albida and treatment 2 (2A: 2M: 2S: 25D) for both M. stenopetala and M. ferruginea exhibited the lowest sturdiness quotient, which indicates that they are good-quality seedling for plantation. The shoot-to-height ratio indicated positive correlation with most seedling growth parameters whereas root collar diameter and sturdiness quotient showed negative correlation for all species. Treatment 3 (3A: 2M: 1S: 75D) is recommended as the best pot soil mix and fertilization compared to all other treatments, though it needs further study.

Evaluating geometric measurement accuracy based on 3D model reconstruction of nursery tomato plants by Agisoft photoscan software

Measuring the vegetation structure of a plant with the help of 3D images is a non-destructive method that can be used to monitor the plant’s status during the growing season. Structure from motion (SfM) is a popular optical distance measurement method that uses 2D images to create 3D models. Identifying factors that influence the precision of SfM is important due to its increasing use in phenotyping. The primary objectives of this study were, i) to scrutinize the impact of imaging parameters—including, color checkerboard location, overlap size of images, and background filtering—on the quality indices of 3D models, and ii) to attain precise measurements of leaf area, stem diameter, and height through the combination of images captured from different camera shooting angles. The three nursery tomato plants were photographed at every five-degree rotation of the turntable, five different locations of color checkerboards, and five camera shooting angles. Results indicated the color checkerboard’s presence and position affect the quality indices of the 3D models. Visually and by the lowest mean keypoint size value, the least noisy 3D model was obtained when the wall of the plant pot was covered with a checkerboard. When the base-to-distance (b/y) ratio as the image overlap index increased from 0.1 to 0.2, the generation time of the 3D models decreased by more than two times. Optimal Mean Absolute Percentage Error (MAPE) values of 1.08 %, 1.01 %, and 1.38 % were achieved for the estimation of leaf area, stem diameter, and height, respectively. This was recorded by using images taken at 0°, 30°, and 105° vertical azimuth angles. Constructing a 3D model by integrating images captured from other angles or more azimuth angles represents numerous visual challenges and leads to problems of point mismatch, which affects the accuracy of measurements.

A chlorophyll-constrained semi-empirical model for estimating leaf area index using a red-edge vegetation index

The parameter of leaf area index (LAI) provides valuable information about plant growth. Most remote sensing methods for estimating LAI are based on the vegetation indices (VIs) in the visible and near-infrared (VNIR) spectral regions. Recently, these VIs calculated with one or more bands in the red-edge region become popular in estimating LAI. Since the reflectance in the red-edge spectral region is sensitive to both LAI and leaf chlorophyll content (LCC), most red-edge-based VIs (such as normalized difference red edge index, NDRE) should be used to estimate the canopy chlorophyll content (the product of LAI and LCC) in theory. When applying NDRE to estimating LAI alone, their relationship is affected by the variation in LCC. To mitigate the LCC effect on LAI estimation using NDRE, this study proposed a chlorophyll-constrained semi-empirical model (CSE). The coefficients of the CSE model were determined from the simulated dataset based on radiative transfer models, and hence were not dependent on measured data. The performance of the CSE model on LAI estimation was evaluated using simulated and measured datasets, including a crop dataset collected in China and a multi-ecosystem dataset obtained from the National Ecological Observatory Network (NEON). The results showed that the root mean square error (RMSE) obtained by the CSE model ranged from 0.58 to 0.72 m2/m2 for the simulated and measured datasets. In particular, the CSE model also exhibited wide generality across multiple vegetation types for the NEON dataset, with the RMSE in the range of 0.33–0.86 m2/m2. In comparison, without considering the LCC effect, the model would lead to clear underestimation or overestimation of LAI (RMSE = 1.26–2.11 m2/m2). These findings improved our understanding of the LCC effects on universal LAI predictive models and would facilitate the accurate mapping of LAI over large regions for monitoring plant growth status.

Estimating Tomato Plant Leaf Area Using Multiple Images from Different Viewing Angles

The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D2, and D3, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D2 and D3 methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D2 and D3 methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.

An automatic method for estimating insect defoliation with visual highlights of consumed leaf tissue regions

As an essential component of the architecture of a plant, leaves are crucial to sustaining decision-making in cultivars and effectively support agricultural processes. When the leaf area is constantly monitored, a plant’s health and productive capacity can be assessed to foment proactive and reactive strategies. Because of that, one of the most critical tasks in agricultural processes is estimating foliar damage. In this sense, we present an automatic method to estimate leaf stress caused by insect herbivory, including damage in border regions. As a novelty, we present a method with well-defined processing steps suitable for numerical analysis and visual inspection of defoliation severity. We describe the proposed method and evaluate its performance concerning 12 different plant species. Experimental results show high assertiveness in estimating leaf area loss with a concordance correlation coefficient of 0.98 for grape, soybean, potato, and strawberry leaves. A classic pattern recognition approach, named template matching, is at the core of the method whose performance is compared to cutting-edge techniques. Results demonstrated that the method achieves foliar damage quantification with precision comparable to deep learning models. The code prepared by the authors is publicly available.

Assessment of morphological attributes of Guava tree for design of a novel tunnel spraying system

The morphological attributes play a significant role in determining the level of applied insecticide by the existing spraying system. Therefore, this attempt was made to assess the various morphological attributes of guava tree such as leaf size, leaf area, leaf area index (LAI) and leaf area density (LAD). In this study, two leaf area estimation techniques (grid count method and Image processing technique based on ImageJ software) were compared and also estimate LAI and LAD for ten trees. An average leaf area was registered 29.56 cm2 and R2 value (0.98) for predicting leaf area by image processing technique. The LAI and LAD varied from 0.69 to 2.17 m2/m2 and 0.53 to 1.89 m2/m3, respectively. In addition, minimum and maximum LAD (mean ± SD) for zone wise ranged from 0.07 ± 0.06 m2/m3 to 2.73 ± 1.43 m2/m3, respectively. The study findings provide an opportunity to deal with large volume of leafs for leaf area estimation and would also help in design of various sprayers like determining the dimensions of tunnel sprayer and allow them to test in laboratory conditions with simulated artificial tree.

Development of Leaf Area Model in Chokeberry Plant Grown in Different Irrigation Water Quality

Chokeberry is a berry fruit species that has grown in the world in a few decades, and it is used in many fields, especially in the pharmaceutical industry. There needs to be a study examining the relationship between leaf area and irrigation water quality in this species, whose cultivation has started to increase. In this study, which was carried out between 2021 and 2022, the effects of irrigation water quality on leaf area in 1-year-old chokeberry plants of the 'Viking' variety grown in peat and soil media were determined. Irrigation water with 0.65 dS m-1 (Control), 2dS m-1, 4dS m-1, 8dS m-1, and 10dS m-1 electrical conductivity were used in the study. A model was developed in this study to estimate leaf area (LA) using leaf width (W) and leaf length (L) values to determine leaf area. The proposed prediction model was determined as "LA=2.86+4.448×W+0.33×L2-1.496×L " (R2= 0.99). Three-dimensional graphs of the developed models were drawn, and the changes in leaf width, length, and leaf area values against irrigation water salinity were determined. As a result, it was determined that there was a negative relationship between the electrical conductivity of irrigation water and leaf width, length, and area values.

Leaf area estimation in Coffea canephora genotypes by neural networks and multiple regression

Leaf area estimation in Coffea canephora genotypes by neural networks and multiple regression