Plant leaf proteins for food applications: Opportunities and challenges.

Plant-based proteins are gaining a lot of attention for their health benefits and are considered as an alternative to animal proteins for developing sustainable food systems. Against the backdrop, ensuring a healthy diet supplemented with good quality protein will be a massive responsibility of governments across the globe. Increasing the yield of food crops has its limitations, including low acceptance of genetically modified crops, land availability for cultivation, and the need for large quantities of agrochemicals. It necessitates the sensible use of existing resources and farm output to derive the proteins. On average, the protein content of plant leaves is similar to that of milk, which can be efficiently tapped for food applications across the globe. There has been limited research on utilizing plant leaf proteins for food product development over the years, which has not been fruitful. However, the current global food production scenario has pushed some leading economies to reconsider the scope of plant leaf proteins with dedicated efforts. It is evident from installing pilot-scale demonstration plants for protein extraction from agro-food residues to cater to the protein demand with product formulation. The present study thoroughly reviews the opportunities and challenges linked to the production of plant leaf proteins, including its nutritional aspects, extraction and purification strategies, anti-nutritional factors, functional and sensory properties in food product development, and finally, its impact on the environment. Practical Application: Plant leaf proteins are one of the sustainable and alternative source of proteins. It can be produced in most of the agroclimatic conditions without requiring much agricultural inputs. It's functional properties are unique and finds application in novel food product formulations.

Environmental photochemistry on plants: recent advances and new opportunities for interdisciplinary research.

Plants play a central role in the photochemistry of chemicals in the environment. They represent a major atmospheric source of volatile organic compounds (VOCs) but also an important environmental surface for the deposition and photochemical reactions of pesticides, gaseous and particulate pollutants. In this review, we point out the role of plant leaves in these processes, as a support affecting the reactions physically and chemically and as a partner through the release of natural constituents (water, secondary metabolites). We discuss the influence of the chosen support (leaves, needle surfaces or fruit cuticles, extracted cuticular waxes and model surfaces) and other factors (additives, pesticides mixture, and secondary metabolites) on the photochemical degradation kinetics and mechanisms. We also show how plants can be a source of photochemically reactive species which can act as photosensitizers promoting the photodegradation of pesticides or the formation and aging of secondary organic aerosols (SOA) and secondary organic materials (SOM). Understanding the fate of chemicals on plants is a research area located at the interface between photochemistry, analytical chemistry, atmospheric chemistry, microbiology and vegetal physiology. Pluridisciplinary approaches are needed to deeply understand these complex phenomena in a comprehensive way. To overcome this challenge, we summarize future research directions which have been clearly overlooked until now.

Chemical characterisation by UPLC-Q-ToF-MS/MS and antibacterial potential of Coffea arabica L. leaves: A coffee by-product.

INTRODUCTION: Coffea arabica L. leaves are considered a by-product of the coffee industry however they are sources of several bioactive compounds. OBJECTIVES: This study aimed to evaluate the chemical composition and the in vitro antibacterial activity of the lyophilised ethanol extract of arabica coffee leaves (EE-CaL). MATERIAL AND METHODS: The chemical characterisation of EE-CaL was performed using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-Q-ToF-MS/MS). The in vitro antibacterial effect of EE-CaL was evaluated using the broth microdilution method and the adapted drop plate agar method to determine the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC), respectively. RESULTS: The chemical analysis of EE-CaL revealed the presence of compounds from the alkaloid class, such as trigonelline and caffeine, in addition to the phenolic compounds such as quinic acid, 5-caffeoylquinic acid, caffeic acid-O-hexoside, mangiferin, (epi)catechin, (epi)catechin monoglucoside and procyanidin trimer. Regarding the antibacterial potential, EE-CaL was active against Gram-positive and Gram-negative bacteria, being more effective against Escherichia coli (ATCC 25922) (MIC = 2500 µg/mL and bactericidal effect). CONCLUSION: The results of this research suggest that coffee leaves, a by-product, possess compounds with antibacterial properties. Thus, further studies with coffee leaf extracts must be carried out to relate the compounds present in the extract with the antibacterial activity and find the mechanisms of action of this extract against bacteria.

Maize metabolome and proteome responses to controlled cold stress partly mimic early-sowing effects in the field and differ from those of Arabidopsis.

In Northern Europe, sowing maize one-month earlier than current agricultural practices may lead to moderate chilling damage. However, studies of the metabolic responses to low, non-freezing, temperatures remain scarce. Here, genetically-diverse maize hybrids (Zea mays, dent inbred lines crossed with a flint inbred line) were cultivated in a growth chamber at optimal temperature and then three decreasing temperatures for 2 days each, as well as in the field. Leaf metabolomic and proteomic profiles were determined. In the growth chamber, 50% of metabolites and 18% of proteins changed between 20 and 16°C. These maize responses, partly differing from those of Arabidopsis to short-term chilling, were mapped on genome-wide metabolic maps. Several metabolites and proteins showed similar variation for all temperature decreases: seven MS-based metabolite signatures and two proteins involved in photosynthesis decreased continuously. Several increasing metabolites or proteins in the growth-chamber chilling conditions showed similar trends in the early-sowing field experiment, including trans-aconitate, three hydroxycinnamate derivatives, a benzoxazinoid, a sucrose synthase, lethal leaf-spot 1 protein, an allene oxide synthase, several glutathione transferases and peroxidases. Hybrid groups based on field biomass were used to search for the metabolite or protein responses differentiating them in growth-chamber conditions, which could be of interest for breeding.

Phytofabricated zinc oxide nanoparticles as a nanofungicide for management of Alternaria blight of Brassica.

Plant pathogens resistant to the commercially available fungicides and bactericides even at higher concentrations are the biggest challenge for the farmers to control the losses due to plant diseases. The antibacterial and antifungal potential of nanomaterials makes them a suitable candidate for the control of plant diseases. Thus, the present study reports the phytofabricated zinc oxide nanoparticles (ZnO Np's) using aqueous plant leaf extract of Terminalia bellerica (Baheda). Characterization of ZnO nanoparticles was done by ultraviolet-visible (UV-Vis) studies, X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infra-red (FT-IR) analysis, and transmission electron microscopy (TEM). The presence of pure hexagonal wurtzite crystalline structure of ZnO nanoparticles was confirmed by XRD analysis. The TEM images revealed the spherical to hexagonal shaped ZnO nanoparticles with sizes ranging from 20 to 30 nm. The stabilization of synthesized ZnO nanoparticles through the interactions of terpenoids, steroids, phenylpropanoids, flavonoids, phenolic acids, and enzymes present in the leaf extract was suggested by FTIR analysis. The mechanism of the formation of ZnO nanoparticles using Terminalia bellerica (Baheda) (Tb-ZnO Np's) as a bioactive compound is proposed. These phytofabricated ZnO nanoparticles (Tb-ZnO Np's) have shown significant antifungal potential against Alternaria brassicae the causal agent of Alternaria blight disease/leaf spot disease in Brassica species. The microscopic results confirm the changes in mycelium morphology and reduction in the number of spore germination at 0.2 mg/mL concentration Tb-ZnO Np's.

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network.

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease's effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

Modified Arrhenius Equation in Materials Science, Chemistry and Biology.

The Arrhenius plot (logarithmic plot vs. inverse temperature) is represented by a straight line if the Arrhenius equation holds. A curved Arrhenius plot (mostly concave) is usually described phenomenologically, often using polynomials of T or 1/T. Many modifications of the Arrhenius equation based on different models have also been published, which fit the experimental data better or worse. This paper proposes two solutions for the concave-curved Arrhenius plot. The first is based on consecutive A→B→C reaction with rate constants k1 ≪ k2 at higher temperatures and k1 ≫ k2 (or at least k1 > k2) at lower temperatures. The second is based on the substitution of the temperature T the by temperature difference T - T0 in the Arrhenius equation, where T0 is the maximum temperature at which the Arrheniusprocess under study does not yet occur.

Real-Time Plant Leaf Counting Using Deep Object Detection Networks.

The use of deep neural networks (DNNs) in plant phenotyping has recently received considerable attention. By using DNNs, valuable insights into plant traits can be readily achieved. While these networks have made considerable advances in plant phenotyping, the results are processed too slowly to allow for real-time decision-making. Therefore, being able to perform plant phenotyping computations in real-time has become a critical part of precision agriculture and agricultural informatics. In this work, we utilize state-of-the-art object detection networks to accurately detect, count, and localize plant leaves in real-time. Our work includes the creation of an annotated dataset of Arabidopsis plants captured using Cannon Rebel XS camera. These images and annotations have been complied and made publicly available. This dataset is then fed into a Tiny-YOLOv3 network for training. The Tiny-YOLOv3 network is then able to converge and accurately perform real-time localization and counting of the leaves. We also create a simple robotics platform based on an Android phone and iRobot create2 to demonstrate the real-time capabilities of the network in the greenhouse. Additionally, a performance comparison is conducted between Tiny-YOLOv3 and Faster R-CNN. Unlike Tiny-YOLOv3, which is a single network that does localization and identification in a single pass, the Faster R-CNN network requires two steps to do localization and identification. While with Tiny-YOLOv3, inference time, F1 Score, and false positive rate (FPR) are improved compared to Faster R-CNN, other measures such as difference in count (DiC) and AP are worsened. Specifically, for our implementation of Tiny-YOLOv3, the inference time is under 0.01 s, the F1 Score is over 0.94, and the FPR is around 24%. Last, transfer learning using Tiny-YOLOv3 to detect larger leaves on a model trained only on smaller leaves is implemented. The main contributions of the paper are in creating dataset (shared with the research community), as well as the trained Tiny-YOLOv3 network for leaf localization and counting.

A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform.

Automatic and efficient plant leaf geometry parameter measurement offers useful information for plant management. The objective of this study was to develop an efficient and effective leaf geometry parameter measurement system based on the Android phone platform. The Android mobile phone was used to process and measure geometric parameters of the leaf, such as length, width, perimeter, and area. First, initial leaf images were pre-processed by some image algorithms, then distortion calibration was proposed to eliminate image distortion. Next, a method for calculating leaf parameters by using the positive circumscribed rectangle of the leaf as a reference object was proposed to improve the measurement accuracy. The results demonstrated that the test distances from 235 to 260 mm and angles from 0 to 45 degrees had little influence on the leafs' geometric parameters. Both lab and outdoor measurements of leaf parameters showed that the developed method and the standard method were highly correlated. In addition, for the same leaf, the results of different mobile phone measurements were not significantly different. The leaf geometry parameter measurement system based on the Android phone platform used for this study could produce high accuracy measurements for leaf geometry parameters.

Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network.

Plant leaf diseases are closely related to people's daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep learning-based method to identify and classify plant leaf diseases. The proposed method can take the advantages of the neural network to extract the characteristics of diseased parts, and thus to classify target disease areas. To address the issues of long training convergence time and too-large model parameters, the traditional convolutional neural network was improved by combining a structure of inception module, a squeeze-and-excitation (SE) module and a global pooling layer to identify diseases. Through the Inception structure, the feature data of the convolutional layer were fused in multi-scales to improve the accuracy on the leaf disease dataset. Finally, the global average pooling layer was used instead of the fully connected layer to reduce the number of model parameters. Compared with some traditional convolutional neural networks, our model yielded better performance and achieved an accuracy of 91.7% on the test data set. At the same time, the number of model parameters and training time have also been greatly reduced. The experimental classification on plant leaf diseases indicated that our method is feasible and effective.

Rapid prediction of chlorophylls and carotenoids content in tea leaves under different levels of nitrogen application based on hyperspectral imaging.

BACKGROUND: Photosynthetic pigments perform critical physiological functions in tea plants. Their content is an essential indicator of photosynthetic efficiency and nutritional status. The present study aimed to predict chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (total Chl), and carotenoid (Car) content in tea leaves under different levels of nitrogen treatment using hyperspectral imaging (HSI) in combination with variable selection algorithms. RESULTS: A total of 150 samples were collected and scanned using the HSI system. The mean spectrum in the region of interest (ROI) was extracted, and the pigment content was measured by traditional chemical methods. Five and seven optimal wavelengths (OWs) were selected using the regression coefficients (RCs) of partial least squares regression (PLSR) and the second-derivative (2-Der), respectively. The optimal 2-Der-PLSR models for Chl a, Chl b, total Chl, and Car performed remarkably well based on seven OWs with correlation coefficients of prediction (RP ) of 0.9337, 0.9322, 0.9333 and 0.9036, root mean square errors in prediction (RMSEP) of 0.1100, 0.0511, 0.1620, and 0.0300 mg g-1 , respectively. CONCLUSION: The results of this study revealed that HSI combined with variable selection method can be employed as a rapid and accurate method for predicting the content of pigments in tea plants. © 2018 Society of Chemical Industry.

Using multiple methods to assess heavy metal pollution in an urban city.

Heavy metal pollution in urban cities is now an accepted fact. An understanding of the natural and anthropogenic contributions to heavy metal accumulation in these cities is necessary to develop strategies to mitigate their impacts, particularly on human health. Here, we used multiple records using geological and biological pollution indicators to assess the extent of pollution in the Colombo Metropolitan Region (CMR), Sri Lanka. Elemental concentrations of Cu, Zn, Ni and Pb were determined in four depositories: surface soil (90 samples), canal sediments and canal water (45 samples each) and vegetation (62 samples). These were mapped using GIS overlapping the road network to identify hotspots of heavy metals. While the surface soil, canal sediments and leaves of trees had higher and different amounts than background levels of heavy metals, canal water had low levels. Our results suggest that anthropogenic activities are the major source of heavy metals in an urban city, and unique natural factors, such as coastal conditions, terrain morphology and climate, combine and influence the distribution of these metals. We discuss the possible remediation of metal pollution and the necessity of a holistic multi-proxy approach to understand urban heavy metal contamination in a rapidly populating area.

Centrifuge Controlled Shape Tuning of Biosynthesized Gold Nanoparticles Obtained from Plumbago zeylanica Leaf Extract.

Development of cost-efficient and eco-friendly biogenic synthetic protocols for the green synthesis of biocompatible metal nanoparticles has become popular among researchers in recent years. The biogenic synthesis of these nanoparticles and their potential biomedical applications introduces the concept of nanobiotechnology, which has become the latest fascinating area of research. The lower cost and lesser side effects as compare to chemical methods of synthesis are the main advantages of biosynthesis. In the present investigation, aqueous leaf extract of Plumbago zeylanica had been used to synthesize anisotropic gold nanoparticles. The as-synthesized gold nanoparticles were centrifuged at 5000 and 10000 rpm and compared both pellets using UV-visible spectroscopy, XRD, FTIR and TEM techniques. We have studied here the effect of speed of centrifugation on the yield, shape, size as well as size distribution of as synthesized gold nanoparticles.

Direct quantitative evaluation of disease symptoms on living plant leaves growing under natural light.

Leaf color is an important indicator when evaluating plant growth and responses to biotic/abiotic stress. Acquisition of images by digital cameras allows analysis and long-term storage of the acquired images. However, under field conditions, where light intensity can fluctuate and other factors (shade, reflection, and background, etc.) vary, stable and reproducible measurement and quantification of leaf color are hard to achieve. Digital scanners provide fixed conditions for obtaining image data, allowing stable and reliable comparison among samples, but require detached plant materials to capture images, and the destructive processes involved often induce deformation of plant materials (curled leaves and faded colors, etc.). In this study, by using a lightweight digital scanner connected to a mobile computer, we obtained digital image data from intact plant leaves grown in natural-light greenhouses without detaching the targets. We took images of soybean leaves infected by Xanthomonas campestris pv. glycines, and distinctively quantified two disease symptoms (brown lesions and yellow halos) using freely available image processing software. The image data were amenable to quantitative and statistical analyses, allowing precise and objective evaluation of disease resistance.

Organ-specific quality control of plant peroxisomes is mediated by autophagy.

Peroxisomes are essential organelles that are characterized by the possession of enzymes that produce hydrogen peroxide (H2O2) as part of their normal catalytic cycle. During the metabolic process, peroxisomal proteins are inevitably damaged by H2O2 and the integrity of the peroxisomes is impaired. Here, we show that autophagy, an intracellular process for vacuolar degradation, selectively degrades dysfunctional peroxisomes. Marked accumulation of peroxisomes was observed in the leaves but not roots of autophagy-related (ATG)-knockout Arabidopsis thaliana mutants. The peroxisomes in leaf cells contained markedly increased levels of catalase in an insoluble and inactive aggregate form. The chemically inducible complementation system in ATG5-knockout Arabidopsis provided the evidence that these accumulated peroxisomes were delivered to vacuoles for degradation by autophagy. Interestingly, autophagosomal membrane structures specifically recognized the abnormal peroxisomes at the site of the aggregates. Thus, autophagy is essential for the quality control of peroxisomes in leaves and for proper plant development under natural growth conditions.

Plant leaf mucilage/carrageenan/Eudragit® NE30D blended films: Optimization, characterization, and pharmaceutical application.

Mucilage of C. pareira leaves was utilized, being manufactured for use in pharmaceutical products. Carrageenan and Eudragit® NE30D were used to combined. Glycerin was used as a plasticizer at a concentration of 20 % w/w based on the amount of polymer used. Computer software optimized its characteristics, including tensile properties, moisture uptake, and erosion; the optimal formulation was 1.4:1.2:2.8. The percentages of optimization error ranged from 8.48 to 13.80 %. Propranolol HCl was mixed to an optimal formulation. The film layer was tight, homogeneous, and smooth, with no holes. DSC thermogram showed no interaction peaks at 101.33 °C and 170.50 °C. Propranolol HCl concentration in the film ranged from 2.18 to 2.20 mg/cm2. Propranolol HCl was quickly released from the film. The kinetic model for the release profile was first-order kinetic. Although propranolol HCl had a high-release profile, its skin permeation was limited. The permeation lag time, Jss, and Kp were 1.60-2.65 h, 0.0182-0.0338 µg/cm2/h, and 9.10-15.35 cm/h, respectively. A significant amount of propranolol HCl residue was found on the skin's surface. Glycerin appeared to influence propranolol HCl permeability. Therefore, the plant leaf mucilage/carrageenan/Eudragit® NE30D blended film can be utilized in pharmaceutical applications to control drug release from its film layer.

Plant leaf disease recognition based on improved SinGAN and improved ResNet34.

The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products, thereby fostering the in-depth development of precision agriculture. However, despite the rapid development of research on plant leaf disease identification, it still faces challenges such as insufficient agricultural datasets and the problem of deep learning-based disease identification models having numerous training parameters and insufficient accuracy. This paper proposes a plant leaf disease identification method based on improved SinGAN and improved ResNet34 to address the aforementioned issues. Firstly, an improved SinGAN called Reconstruction-Based Single Image Generation Network (ReSinGN) is proposed for image enhancement. This network accelerates model training speed by using an autoencoder to replace the GAN in the SinGAN and incorporates a Convolutional Block Attention Module (CBAM) into the autoencoder to more accurately capture important features and structural information in the images. Random pixel Shuffling are introduced in ReSinGN to enable the model to learn richer data representations, further enhancing the quality of generated images. Secondly, an improved ResNet34 is proposed for plant leaf disease identification. This involves adding CBAM modules to the ResNet34 to alleviate the limitations of parameter sharing, replacing the ReLU activation function with LeakyReLU activation function to address the problem of neuron death, and utilizing transfer learning-based training methods to accelerate network training speed. This paper takes tomato leaf diseases as the experimental subject, and the experimental results demonstrate that: (1) ReSinGN generates high-quality images at least 44.6 times faster in training speed compared to SinGAN. (2) The Tenengrad score of images generated by the ReSinGN model is 67.3, which is improved by 30.2 compared to the SinGAN, resulting in clearer images. (3) ReSinGN model with random pixel Shuffling outperforms SinGAN in both image clarity and distortion, achieving the optimal balance between image clarity and distortion. (4) The improved ResNet34 achieved an average recognition accuracy, recognition precision, recognition accuracy (redundant as it's similar to precision), recall, and F1 score of 98.57, 96.57, 98.68, 97.7, and 98.17%, respectively, for tomato leaf disease identification. Compared to the original ResNet34, this represents enhancements of 3.65, 4.66, 0.88, 4.1, and 2.47%, respectively.

Characterization of Decellularized Plant Leaf as an Emerging Biomaterial Platform.

Decellularized plants have emerged as promising biomaterials for cell culture and tissue engineering applications due to their distinct material characteristics. This study explores the biochemical, mechanical, and structural properties of decellularized leaves that make them useful as biomaterials for cell culture. Five monocot leaf species were decellularized via alkali treatment, resulting in the effective removal of DNA and proteins. The Van Soest method was used to quantitatively evaluate the changes in cellulose, hemicellulose, and lignin content during decellularization. Tensile tests revealed considerable variations in mechanical strength depending on the plant species, the decellularization state, and the direction of applied mechanical force. Decellularized monocot leaves exhibited a notable reduction in mechanical strength and anisotropic properties depending on the leaf orientation. Imaging revealed inherent microgrooves on the epidermis of the monocot leaves. Permeability studies, including water uptake and biomolecule transport through decellularized leaves, confirmed excellent water uptake capability but limited biomolecule transport. Lastly, the plants were enzymatically degradable using typical plant enzymes, which were minimally cytotoxic to mammalian cells. Taken together, the features of decellularized plant leaves characterized in this study suggest ways in which they can be useful in cell culture and tissue engineering applications.

Multi-classification of disease induced in plant leaf using chronological Flamingo search optimization with transfer learning.

Agriculture is imperative research in visual detection through computers. Here, the disease in plants can distress the quality and cultivation of farming. Earlier detection of disease lessens economic losses and provides better crop yield. Detection of disease from crops manually is an expensive and time-consuming task. A new scheme is devised for accomplishing multi-classification of disease using plant leaf images considering the chronological Flamingo search algorithm (CFSA) with transfer learning (TL). The leaf image undergoes pre-processing using Adaptive Anisotropic diffusion to discard noise. Here, the segmentation of plant leaf is done with U-Net++, and trained by the Moving Gorilla Remora algorithm (MGRA). The image augmentation is further applied considering two techniques namely position augmentation and color augmentation to reduce data dimensionality. Thereafter the feature mining is done to produce crucial features. Next, the classification in terms of the first level is considered for classifying plant type and classification in terms of the second level is done to categorize disease using convolutional neural network (CNN)-based TL with LeNet and it undergoes training using CFSA. The CFSA-TL-based CNN with LeNet provided better accuracy of 95.7%, sensitivity of 96.5% and specificity of 94.7%. Thus, this model is better for earlier plant leaf disease detection.

A novel hierarchical framework for plant leaf disease detection using residual vision transformer.

Early detection of plant leaf diseases accurately and promptly is very crucial for safeguarding agricultural crop productivity and ensuring food security. During their life cycle, plant leaves get diseased because of multiple factors like bacteria, fungi, weather conditions, etc. In this work, the authors propose a model that aids in the early detection of leaf diseases using a novel hierarchical residual vision transformer using improved Vision Transformer and ResNet9 models. The proposed model can extract more meaningful and discriminating details by reducing the number of trainable parameters with a smaller number of computations. The proposed method is evaluated on the Local Crop dataset, Plant Village dataset, and Extended Plant Village Dataset with 13, 38, and 51 different leaf disease classes. The proposed model is trained using the best trail parameters of Improved Vision Transformer and classified the features using ResNet 9. Performance evaluation is carried out on a wide aspects over the aforementioned datasets and results revealed that the proposed model outperforms other models such as InceptionV3, MobileNetV2, and ResNet50.