Plant Leaf Research Articles

Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning

In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, and the identification of plant species. The Cope Leaf Dataset offers a comprehensive collection of leaf images from various plant species, enabling the development and evaluation of advanced classification algorithms. This study presents a robust methodology for classifying leaf images within the Cope Leaf Dataset by enhancing the feature extraction and selection process. Cope Leaf Dataset has 99 classes and 64 features with 1584 records. Features are extracted based on the margin, texture, and shape of the leaves. It is challenging to classify a large number of labels because of class imbalance, feature complexity, overfitting, and label noise. Our approach combines advanced feature selection techniques with robust preprocessing methods, including normalization, imputation, and noise reduction. By systematically integrating these techniques, we aim to reduce dimensionality, eliminate irrelevant or redundant features, and improve data quality. Increasing accuracy in classification, especially when dealing with large datasets and many classes, involves a combination of data preprocessing, model selection, regularization techniques, and fine-tuning. The results indicate that the Multilayer Perception algorithm gives 89.48%, the Naïve Bayes Classifier gives 89.63%, Convolutional Neural Networks has 88.72%, and the Hoeffding Tree algorithm gives 89.92% accuracy for the classification of 99 label plant leaf classification problems.

Insecticidal efficacy of green synthesized silver nanoparticles, Bacillus thuringiensis and Triazophos against Pectinophora gossypiella

Pink bollworm (Pectinophora gossypiella) poses a significant threat to cotton crops worldwide, resulting in economic losses surpassing sustainable levels for farmers since 2011. This study examines different approaches to controlling pink bollworm infestations, specifically silver nanoparticles (AgNPs), bio-formulations of Bacillus thuringiensis (Bt), and a commercial insecticide (Triazophos), in both laboratory and field environments. AgNPs were synthesized using neem plant leaf extract (Azadirachta indica). The formation of AgNPs was confirmed through UV-visible spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and a zeta sizer analyzer. In the laboratory, the effectiveness of individual and combined applications of AgNPs, Bt, and Triazophos was tested against 2nd and 4th instar pink bollworm larvae. Mortality rates were measured at 1, 2, and 5 days after application. The results showed that the treatment combining AgNPs and Triazophos (40ppm + 50ppm) achieved a larval mortality rate of over 90%, surpassing the other treatments. In field conditions, the combination of Triazophos and AgNPs at 40ppm (100ml + 50ml) proved most effective in managing P. gossypiella populations. These findings underscore the potential of nanoparticles as efficient larvicidal agents for controlling P. gossypiella. Furthermore, they emphasize the importance of reevaluating past management strategies for cotton crops given emerging alternatives to traditional insecticides. Future research directions may involve further optimizing nanoparticle formulations and investigating their broader ecological impacts on cotton ecosystems.

Calculation Method Based on Flower Petal Area and Other Plant Leaf Spot Area

In order to calculate the area of irregular shapes such as plant leaves and diseased spots or flower petals, this paper presents a new method to calculate them by using flood fill algorithm, HSV color space, improved k-means algorithm and morphological operation. First, 501 butterfly petal images and pathological leaf images of Bauhinia and phyllotaxus were collected, and then flood was used Fill algorithm selects the disease-free area and records the selected pixel value. HSV color space conversion is applied to the image to facilitate the segmentation of leaves. Then, the improved k-means algorithm is used to extract the binary image of leaves and record the pixel value of the outer contour with morphological closed operation. Finally, the proportion and truth of the disease spots of plant leaves are obtained by calculating the pixel value and the real value of the rectangle in the sampling area Real area. Compared with the results of artificial labeling, the average accuracy of petal area and lesion area of Phalaenopsis was 96.3% and 96.61%, respectively. It can be seen that the program can calculate the area of irregular shape of plant surface accurately. In conclusion, this method can replace the artificial grid method to calculate the information of plant leaf area and disease proportion, and effectively reduce the work intensity of experimental personnel.

Influence of Training and Tomato Hybrids on Growth, and Quality of Tomato under Shade Net House

This study examines the influence of different training systems and tomato hybrids on the growth, yield, and quality of tomatoes (Solanum Lycopersicon L.) under shade net house conditions. It was conducted at the Hi-Tech Horticulture Unit, Centre of Excellence for Vegetables, Karnal, during the 2016-17 growing season. The experiment followed a Factorial arrangement of treatments in Randomized Complete Block Design (FRBD) with two training systems (one-stem and two-stem) and four hybrids (STH-39, STH-901, STH-801, and STH-701). Growth results indicated that the one-stem system significantly enhanced plant height (270.08 cm), leaf area (77.06 cm²), and stem girth (1.75 cm), while the two-stem system increased the number of leaves (82.80 per plant). Hybrid STH-801 exhibited superior vegetative performance, with the highest plant height (309.03 cm), maximum number of branches (8.17), and largest leaf area (79.22 cm²). In terms of quality parameters, the training systems had no significant effect on traits such as total soluble solids (TSS), pH, pericarp thickness, or shelf life. However, hybrid STH-801 recorded the highest TSS (5.47 °Brix) and longest shelf life (23 days). STH-39 produced the thickest pericarp (1.10 cm) and largest fruit volume (157 cc), contributing to better fruit quality. These findings highlight the importance of hybrid selection and training systems in improving both the growth and quality of tomatoes under protected cultivation.

Cucumber Leaf Segmentation Based on Bilayer Convolutional Network

When monitoring crop growth using top-down images of the plant canopies, leaves in agricultural fields appear very dense and significantly overlap each other. Moreover, the image can be affected by external conditions such as background environment and light intensity, impacting the effectiveness of image segmentation. To address the challenge of segmenting dense and overlapping plant leaves under natural lighting conditions, this study employed a Bilayer Convolutional Network (BCNet) method for accurate leaf segmentation across various lighting environments. The major contributions of this study are as follows: (1) Utilized Fully Convolutional Object Detection (FCOS) for plant leaf detection, incorporating ResNet-50 with the Convolutional Block Attention Module (CBAM) and Feature Pyramid Network (FPN) to enhance Region of Interest (RoI) feature extraction from canopy top-view images. (2) Extracted the sub-region of the RoI based on the position of the detection box, using this region as input for the BCNet, ensuring precise segmentation. (3) Utilized instance segmentation of canopy top-view images using BCNet, improving segmentation accuracy. (4) Applied the Varifocal Loss Function to improve the classification loss function in FCOS, leading to better performance metrics. The experimental results on cucumber canopy top-view images captured in glass greenhouse and plastic greenhouse environments show that our method is highly effective. For cucumber leaves at different growth stages and under various lighting conditions, the Precision, Recall and Average Precision (AP) metrics for object recognition are 97%, 94% and 96.57%, respectively. For instance segmentation, the Precision, Recall and Average Precision (AP) metrics are 87%, 83% and 84.71%, respectively. Our algorithm outperforms commonly used deep learning algorithms such as Faster R-CNN, Mask R-CNN, YOLOv4 and PANet, showcasing its superior capability in complex agricultural settings. The results of this study demonstrate the potential of our method for accurate recognition and segmentation of highly overlapping leaves in diverse agricultural environments, significantly contributing to the application of deep learning algorithms in smart agriculture.

An intelligent agriculture monitoring framework for leaf disease detection using YOLOv7

Agriculture is one of the most important economic sectors on which societies have relied since ancient times. With the recent development of technology, agriculture has also been incorporating modern techniques such as the Internet of Things and Artificial Intelligence to improve productivity and monitor the farming process. One of agriculture’s most prominent issues is the spread of plant diseases and the lack of real-time monitoring. Various systems and operations have recently been developed to predict and diagnose plant diseases. However, current operations have been selective, focusing on a specific aspect without addressing other important aspects, resulting in either partial or compound application of results, rendering the desired outcomes ineffective. To deal with such challenges, we propose an intelligent framework for real-time agriculture monitoring and disease detection, namely a system for monitoring plant diseases using YOLOv7. In the proposed framework, a rule-based policy has been designed for detecting plant diseases using online plant leaf monitoring, sensors, and surveillance cameras. Images of plant leaves captured by different cameras are sent in real-time to central cloud servers for disease detection. The improved YOLOv7 technology is utilized for plant disease detection, and the proposed system has been evaluated using a dataset of diseased tomato leaves, comparing it with different models based on various performance metrics to demonstrate its effectiveness, achieving an accuracy of 96%.

An optimization method for measuring the stomata in cassava (Manihot esculenta Crantz) under multiple abiotic stresses.

As a gateway for gas exchange, pores regulate the transport of air and water in carbon assimilation, respiration, and transpiration to quickly adapt to environmental changes. Therefore, the study of stomatal movement characteristics of plants is helpful to strengthen the understanding of the mechanism of plant response to multi-environmental stress, and can improve the function of plant resistance to stresses. The stomatal movement of Arabidopsis leaves was observed by staining the stomata with rhodamine 6G, but this method has not been reported in other plant leaf stomata studies. Taking cassava as an example, the correlation between cassava stomatal movement and cassava response to stress was observed by using and improving the staining method. Rhodamine 6G is a biological stain widely used in cell biology and molecular biology. It was found that 1 μM rhodamine 6G could stain the stomata of cassava without affecting stomatal movement (n = 109, p < 0.05). In addition, we proposed that stomata fixed with 4% concentration of formaldehyde after staining were closest to the stomatal morphology of cassava epidermis, so as to observe stomatal movement under different environmental stresses more accurately. Previous methods of measuring stomatal pore size by autofluorescence of cell wall needs to fix the cells for 6 h, but Rhodamine staining can only be observed in 2 min, which greatly improves the experimental efficiency. Compared with the traditional exfoliation method (e.g., Arabidopsis), this method can reduce the damage of the leaves and observe the stomata of the whole leaves more completely, so that the experimental results are more complete. In addition, the method enables continuous leaf processing and observation. Using this method, we further compared four different cassava varieties (i.e., KU50, SC16, SC8, and SC205) and found that there are differences in stomatal density (SD) among cassava varieties, and the difference in the SD directly affects the stress resistance of cassava (n = 107, p < 0.001). This finding has important implications for studying the mechanism of plant response to environmental stress through stomata.

Determination of Salt Stress by Plant Leaf Temperature and Thermal Imaging in Black Cumin Grown Under Different Salt Sources

ABSTRACT Thermal imaging has been used in recent years to determine salinity stress in certain crops. However, no information is available on this regarding black cumin (Nigella sativa L.). The main purpose of this study was to investigate the determinability of the salt stress on black cumin by thermal imaging. The leaf temperature values were obtained by acquiring the thermal images for black cumin grown under different irrigation water salinity levels (0.6 as control, 1.5, 2.5, and 5.0 dS/m) prepared for different salt sources (CaCl2, MgCl2, NaCl, Ca(NO3)2, MgSO,4 and Na2SO4). Plant leaf temperatures averaged over all irrigation water salinity levels did not show a significant difference among salt sources. On the other hand, plant leaf temperatures independent of the salt sources significantly increased with increasing salinity levels. The order for salinity levels in terms of their effect on the leaf temperature of black cumin was determined as 5.0 dS/m > 2.5 dS/m > 1.5 dS/m ≥ 0.6 dS/m. The highest leaf temperature was obtained under 5.0 dS/m salinity level with CaCl2, MgCl2, Ca(NO3)2, MgSO4, and Na2SO4 salt sources, but the values for MgSO4 and Na2SO4 salt sources were not significantly different from those under 2.5 dS/m salinity. In addition, a strong-negative relationship between plant water consumption and leaf temperature under CaCl2 and MgSO4 and a moderate-negative relationship under MgCl2 and NaCl salt sources were determined. The results of this study showed that the plant leaf temperature values obtained by thermal imaging reflected salt stress conditions, especially under high salinity levels.

Activity of Antioxidant Enzymes and Expression of the Genes Encoding Them in Leaves of Wheat Plants with Different Allelic Status of the GPC-B1 Gene with Optimal Zinc Content in the Environment and Its Deficiency

Activity of Antioxidant Enzymes and Expression of the Genes Encoding Them in Leaves of Wheat Plants with Different Allelic Status of the GPC-B1 Gene with Optimal Zinc Content in the Environment and Its Deficiency

Physiological and Biochemical Responses of ‘Burlat’ Sweet Cherry to Pre-Harvest Foliar Application of Calcium and Seaweed Extracts

Sweet cherry (Prunus avium L.) is a highly valued fruit, and optimal nutrient management is crucial for enhancing yield and fruit quality. However, the over-application of chemical fertilizers in cherry cultivation leads to environmental issues such as soil degradation and nutrient runoff. To address this, foliar application, a more targeted and eco-friendly fertilization method, presents a promising alternative. This study evaluates the effects of pre-harvest foliar application of calcium (Ca) (150 and 300 g hL−1) and seaweed extracts (75 and 150 mL hL−1), both individually and in combination, on the physiological and biochemical responses of ‘Burlat’ sweet cherry trees. Key physiological parameters, including plant water status, photosynthetic performance, and leaf metabolites, were analyzed. Results show that trees treated with seaweed extracts or with combined Ca and seaweed application had improved water status, higher sugar, starch, and protein content, as well as enhanced antioxidant activity and phenolic content compared to those treated solely with calcium. However, the combined treatment did not significantly enhance overall tree performance compared to individual applications. This study highlights the potential of seaweed-based biostimulants in sustainable cherry production.

Crop Leaf Type Classification and Multi-Class Leaf Disease Identification with Tangent Hunter Prey Optimization-Based LeNet

The early detection and accurate rate of classification of plant leaf disease are more important for reliable and good agriculture, which prevents unwanted financial loss and resources. In this study, Tangent Hunter Prey Optimization-based LeNet (THPO-LeNet) is utilized for crop leaf classification and multi-class plant leaf disease identification. In this case, the input image that goes through the image pre-processing step is obtained from the plant village dataset. An adaptive Wiener filter is applied at the pre-processing stage to reduce needless mistakes and improve image quality. The pre-processed image is then sent to the leaf segmentation step, where a Mask Region-based Convolution Neural Network (Mask R-CNN) performs the segmentation. Consequently, image augmentation is performed using techniques such as scaling, rotation, translation, flipping, contrast, saturation, and hue. Afterwards, first-level classification plant leaf classification is processed by LeNet, which is optimized by Tangent Hunter Prey Optimization (THPO). The THPO is the incorporation of a Tangent Search Algorithm (TSA) and Hunter–Prey Optimizer (HPO). At last, the second-level classification of plant leaf disease is conducted by THPO-LeNet. Furthermore, the efficiency of THPO-LeNet is examined based on measures, like accuracy, Negative Predictive Value (NPV), True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), loss value, and False Positive Rate (FPR), and the value attained is 95.68%, 92.50%, 91.48%, 92.25%, 92.54%, 8.321%, and 7.754%, respectively.

Green synthesis of silver nanoparticle by Hyoscyamus muticus L. extract and study of its effect on tomato infected with Meloidogyne javanica

In this study, the aqueous leaf extract of Hyoscyamus muticus L Plant leaf was prepared using three methods: maceration (MAC), microwave-assisted extraction (MAE), and ultrasonic-assisted extraction (UAE). Then, the extracts were used to synthesize silver nanoparticles (Ag NPs) in an environmentally friendly approach. The characterization of the synthesized Ag NPs was carried out by various techniques. The XRD results confirmed the successful synthesis of nano-sized Ag crystals with spherical structure. The SEM images showed the formation of Ag NPs, which were smaller than 75 nm. The UV-Vis studies showed an obvious absorption peak for Ag NPs between 400-500 nm, with a clear peak around 450 nm for all samples. The elemental analysis using the EDS technique confirmed the presence of silver in the synthesized Ag NPs. Furthermore, the effect of different concentrations of Ag NPs was investigated on hatching and second-stage juvenile (J2s) mortality of Meloidogyne javanica, under controlled conditions. The results showed that increasing concentrations of Ag NPs led to higher percentages of inhibition of egg hatching and J2s mortality. Additionally, the Ag NPs synthesized by ultrasonic-assisted extraction with lower lethal concentration were found to be more toxic to M. javanica than the extract prepared by soaking. According to the results, Ag NPs showed a stronger inhibitory effect on M. javanica egg hatching and J2s mortality compared to the total extracts and chemically synthesized Ag NPs. Also, the lowest LC50 was observed for the Ag NPs synthesized using the plant extract as: UAE > MAE (180 w) > MAE (90 w) > MAC methods. The highest inhibition of egg hatch occurred at 0.5 % v/v of Ag NPs after 72 hours and remained consistent after 120 hours.

Wound-Healing Activity of Solvent Fractions and Antiinflammatory Activity of Crude Extract and Solvent Fractions of Acokanthera schimperi Schweinf (Apocynaceae) Leaves in Mice Model

BackgroundIn Ethiopia, Acokanthera schimperi is frequently used to treat wounds and other conditions. Nothing has been published to date on the anti-inflammatory properties of A. schimperi leaves or the wound-healing effects of solvent fractions, despite a report on the crude extract's ability to heal wounds being available in the literature. PurposeTherefore, this experiment has been initiated to look into the solvent fractions' ability to heal wounds and the anti-inflammatory properties of the A. schimperi leaf 80% methanol extract and its fractions in mice. Study designThe experimental study design was conducted to evaluate wound healing and the anti-inflammatory properties of the A. schimperi leaf 80% methanol extract and its fractions in mice. MethodsThe plant's leaf crude extract was fractionated using ethyl acetate, chloroform, and distilled water. The resulting fractions were then combined with a simple ointment base to make an ointment at 5% and 10% w/w. Next, using excision and incision wound models, the ointments' ability to promote wound healing was assessed. The excision model was utilised to estimate wound contraction and epithelization time, whereas the incision model was utilised to measure tensile strength. By dissolving 1% carrageenan in 0.9% saline (w/v) and giving oral dosages of the crude extract and solvent fractions (100, 200, and 400 mg/kg), the anti-inflammatory efficacy of the leaves was assessed concurrently with a carrageenan-induced hind paw edema model. ResultsSignificant wound healing activity was seen in both models in wounds treated with 5% and 10% (w/w) solvent fraction ointment, as shown by higher tissue breaking strength (with 51.38% maximum effect by 10% (w/w) ethyl acetate fraction ointment), shorter epithelization times (within 15.92 days by ethyl acetate fraction ointment 10%), and increased wound contraction (99.40% by ethyl acetate fraction ointment 10%). The extract (hydromethanolic) and solvent fractions also showed notable dose-related decreases in inflammation. ConclusionThe study's findings supported A. schimperi's traditional use as a wound healing agent by showing that the solvent fractions of the plant's leaves promoted wound healing, at least in part, through their anti-inflammatory effect.

Mixed application of raw amino acid powder and Trichoderma harzianum fertilizer for the prevention and management of apple replant disease

Apple replant disease (ARD) is mainly caused by biological factors, and it severely restricts the development of the apple industry. The use of biological control measures to alleviate ARD is critically important for the sustainable development of the apple industry. The effects of raw amino acid powder and Trichoderma harzianum fertilizer on plant biomass, leaf and root indexes, soil physical and chemical properties, soil enzyme activity, and the soil fungal community were studied under pot and field conditions using Malus hupehensis Rehd. seedlings and grafted trees (Fuji New 2001/M9T337) as experimental materials. We found that the application of the materials significantly promoted plant growth, increased the leaf photosynthesis and chlorophyll content, root respiration rate, root antioxidant enzyme activity, and soil enzyme activity, significantly decreased the number of Fusarium sp. in soil, and significantly increased the abundance of beneficial bacteria. In conclusion, the mixed application of raw amino acid powder and Trichoderma harzianum fertilizer can is an effective method for the prevention and management of ARD.

Effect of Biostimulants on Plant Growth and Leaf Functional Compounds of Passiflora Plants

The influence of biostimulant treatments on Passiflora species was assessed by observing changes in the growth and leaf functional compounds of plants exposed to humic acids (HAs) and seaweed extracts (SEs) to determine their optimal application doses. Four different concentrations of HA irrigation (0, 144, 192, and 285 mg·L−1 per pot assigned as HI-1, 2, 3, and 4, respectively), HA spraying (0, 96, 128, and 192 mg·L−1 as HS-1, 2, 3, and 4, respectively), SE irrigation (SI-1∼4 at 0, 1, 1.33, and 2 g·L−1 per pot, respectively), and SE spraying (SS-1∼4 at 0, 0.5, 0.67, and 1 g·L−1, respectively) treatments were applied to ‘Tainung No. 1’ and Passiflora suberosa cultivars every week to study their responses in plant growth traits and metabolites. Results show that leaf dry weight (DW) and root DW of ‘Tainung No. 1’ plants under SS-3 and HI-4 treatments, respectively, were significantly increased compared with controls. Both HI-3 and HS-3 treatments remarkably increased vine length, fresh weight (FW), and DW of leaves and shoots in P. suberosa plants compared with controls. Furthermore, ‘Tainung No. 1’ and P. suberosa plants respectively subjected to HI-2 and SS-4 treatments contributed to developing biostimulant applications for obtaining higher total phenol and flavonoid content per plant. Both Passiflora species are recommended for treatment with SS-4 and SE to maximize orientin and isovitexin content, and for all their beneficial applications and sustainable use in agriculture.

Phytochemical and toxicological evaluation of aqueous leaf extract of Premna integrifolia L. in male balb/c mice.

BackgroundPhytoconstituents present in plants provide a natural and comprehensive method for combating oxidative stress and various ailments. Safety evaluation of these herbal medications is necessary, so acute and sub-acute toxicity of plant leaf extract Premna integrifolia has been performed to standardize its dose regarding its therapeutic application in male fertility. PurposeToxicological evaluation Aqueous leaf extract of Premna integrifolia (ALEPI), a known herbal medicine, for its utilization in male fertility enhancement use. Study design and methodsTo assess the safety and pharmacological potential of the ALEPI, acute and subacute toxicity studies were carried out in compliance with the OECD guidelines No. 425 and 407, respectively. Identification of pharmacologically active compounds in ALEPI was performed using UHPLC-HRMS analysis. In vitro,estimation of Polyphenol Content, Total Flavonoid Content, saponins and Antioxidant Properties of ALEPI was performed. The oral acute toxicity of the ALEPI was conducted using male mice. The study involved administering single doses of ALEPI at 300, 2000, and 5000 mg/kg b.wt., followed by a fourteen-day observation period to monitor any signs of toxicity or adverse effects. In the oral subacute toxicity study, mice received daily oral gavage of the ALEPI at doses of 400, 800, and 1000 mg/kg b.wt. for twenty-eight days. The parameters studied included total reactive oxygen species (ROS) production, apoptosis, cell viability percentage, and cellular morphology. ResultsThe UHPLC-HRMS analysis of the ALEPI reveals the presence of various bioactive compounds with significant antioxidant, anti-inflammatory, and anticancer properties. The acute and subacute toxicity studies of the ALEPI provided a comprehensive evaluation of its safety profile. Both studies demonstrated no mortality and only minor alterations in relative organ weights, hematology, and serum biochemistry, suggesting a low toxicity profile. Additionally, the normal histoarchitecture of vital organs indicates the absence of significant morphological changes. Significant reduction in total ROS, necrosis percentage, and apoptosis in testicular tissue compared to the control group highlights the antioxidant properties of the extract. ConclusionBased on the findings from both the acute and subacute toxicity studies, it is evident that the ALEPI does not actuate adverse effects of toxicological significance at the tested doses and hence can be used at appropriate concentrations in therapeutic application.

Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

Selecting techniques is a crucial aspect of disease detection analysis, particularly in the convergence of computer vision and agricultural technology. Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security. Deep learning is a viable answer to meet this need. To proceed with this study, we have developed and evaluated a disease detection model using a novel ensemble technique. We propose to introduce DenseNetMini, a smaller version of DenseNet. We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning. Another unique proposition involves utilizing Gradient Product (GP) as an optimization technique, effectively reducing the training time and improving the model performance. Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements. Test accuracy rates of 99.65 %, 98.96 %, and 98.11 % are seen in the Plantvillage, Tomato leaf, and Appleleaf9 datasets, respectively. One of the research's main achievements is the significant decrease in processing time, which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency. Beyond quantitative successes, the study highlights Explainable Artificial Intelligence (XAI) methods, which are essential to improving the disease detection model's interpretability and transparency. XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification, which promotes confidence and understanding of the model's functionality.

Non-invasive laser bio-speckle technique for the study of optical irradiation on plant leaf lamina: Application to monitor salicylic acid modulated response in Zamioculcas zamiifolia

Non-invasive laser bio-speckle technique for the study of optical irradiation on plant leaf lamina: Application to monitor salicylic acid modulated response in Zamioculcas zamiifolia

Historical increases of maize leaf area index in the US Corn Belt due primarily to plant density increases

ContextLeaf area index (LAI) and leaf area distribution within the maize plant are important traits used to explain and predict light interception and thus crop productivity. ObjectivesHere we investigate breeding and plant density effects of leaf area traits. Our objectives are to 1) quantify maize breeding impacts on leaf area distribution and determine bell-shape coefficients used in crop modeling, 2) dissect the contribution of breeding from plant density, and 3) explore the relationship between LAI and crop yields. MethodsWe studied 18 hybrids released between 1983 and 2017 at two density treatments: current (8.5 pl m-2) and historical increasing density (from 4.6 to 8.5 pl m-2) in Iowa, USA. ResultsResults indicated that concurrent changes in hybrids and increases in plant density have increased LAI from 3.4 (in 1983) to 5.9 m2 m-2 (in 2017), with the highest LAI increases (>50%) to be realized in the middle canopy. At historical increasing in plant density treatment, the LAI increased by 1.6% year-1, but the individual plant leaf area decreased by 0.33% year-1 from 1983 to 2017. This trade-off indicates that new hybrids are more tolerant to higher plant populations than old hybrids. At current plant density treatment, the year of hybrid release did not affect LAI or individual plant leaf area. New hybrids had 5% narrower leaf area distributions, 23% higher optimum LAI values (5.2 vs 4.2 m2 m-2) and 19% higher grain yields compared to old hybrids. ConclusionsThe main reason for the increase in maize LAI in the US Corn Belt is plant density. However, an increase in LAI does not necessarily translate to higher grain yields as new hybrids had significantly higher grain yields than older hybrids at similar LAI values. Present results contribute to our understanding of maize canopy architecture and allow us to better calibrate crop models to accurately estimate LAI and grain yield.

Metallo‐Surfactant Complex‐Assisted Biomimetic Synthesis of Silver Nanoparticles: Exploring Relativistic Effects in Catalytic and Sensing Applications

AbstractA biological reduction method for silver nanoparticles was employed using Cassia alata extract from plant leaves, which functions as a reducing agent and the metallo‐surfactant [Co(dqn)2(C12H27N)2]3+ (dqn = dipyrido[3,2‐f:2′,3′‐h]‐quinoxaline; C12H27N)2 = dodecylamine) acting as both stabilizing and capping agent. The ratio of Ag nanoparticles (AgNPs) formation was adjusted to be equal to the amount of AgNO3, along with variations in the amount of plant leaf extract, the metallo‐surfactant, pH, surrounding temperature, and the length of interaction periods. High‐resolution transmission electron microscopy (HRTEM), field emission scanning electron microscopy (FE‐SEM), energy‐dispersive spectroscopy (EDS), and energy‐dispersive X‐ray analysis (EDAX) were used to confirm the formation of AgNPs. The infrared results indicate the presence of hydroxyl, amine, and carboxylate groups in the extract plays a crucial role in the reduction process. Additionally, the metallo‐surfactant acts as a capping agent for the silver nanoparticles, preventing agglomeration. By adjusting the acidity of the solution and the quantity of the additive metallo‐surface active agent utilized, the size of AgNPs can be precisely regulated. The relativistic effects observed in this metallo‐surfactant‐assisted silver nanoparticle demonstrate excellent reduction capabilities for nitro compounds, effective dye degradation, and mercury sensing applications.