Apple Leaf Research Articles

Disease diagnostic method based on cascade backbone network for apple leaf disease classification.

Fruit tree diseases are one of the major agricultural disasters in China. With the popularity of smartphones, there is a trend to use mobile devices to identify agricultural pests and diseases. In order to identify leaf diseases of apples more easily and efficiently, this paper proposes a cascade backbone network-based (CBNet) disease identification method to detect leaf diseases of apple trees in the field. The method first replaces traditional convolutional blocks with MobileViT-based convolutional blocks particularly for feature extraction. Compared with the traditional convolutional block, the MobileViT-based convolutional block is able to mine feature information in the image better. In order to refine the mined feature information, a feature refinement module is proposed in this paper. At the same time, this paper proposes a cascaded backbone network for effective fusion of features using a pyramidal cascaded multiplication operation. The results conducted on field datasets collected using mobile devices showed that the network proposed in this paper can achieve 96.76% accuracy and 96.71% F1-score. To the best of our knowledge, this paper is the first to introduce Transformer into apple leaf disease identification, and the results are promising.

多类别苹果叶病快速检测识别研究

Apple planting process is often accompanied by the impact of a variety of diseases. A single apple leaf often presents the situation of multiple diseases occurring at the same time, which brings great challenges to fruit farmers' rapid diagnosis and correct control. In this paper, aiming at the rapid detection and recognition of multi-category apple leaf disease, a multi-target detection model is constructed to realize the rapid detection and recognition of single leaf and multi leaf, single disease and multi disease. Through the technical means of manual labeling, data enhancement and parameter optimization, Yolo v4, SSD and Efficientdet are selected to train and evaluate the apple leaf disease data set. The results show that the target detection model based on Yolo v4 achieves better training effect, and its mAP value is 83.34%. The model can meet the needs of rapid disease spot detection and recognition of single leaf single disease and multi leaf multi disease in natural environment.

基于改进卷积神经网络的田间苹果叶片病害识别

In view of the obvious differences in the manifestations of the same diseases in apples at different stages of the disease, different diseases show certain similarities, and the early symptoms of the disease are not obvious. For these problems, a new model attention residual network (ARNet) was introduced based on the combination of attention and residual thought. The model introduces the multi-layer attention modules to solve the problems of early disease location dispersion and features that are difficult to extract. In order to avoid network degradation, a residual module was constructed to effectively integrate high and low-level features, and data augment technology was introduced to prevent the model from over-fitting. The proposed model (ARNet) achieved an average accuracy of 99.49% on the test set of 4 kinds of apple leaf diseases with real complex backgrounds. Compared with the models ResNet50 (99.19%) and MobileNetV2 (98.17%), it had better classification performance. The model proposed in this paper had strong robustness and high stability and can provide a reference for the intelligent diagnosis of apple leaf diseases in practical applications.

Comparison of various deep convolutional neural network models to discriminate apple leaf diseases using transfer learning

Comparison of various deep convolutional neural network models to discriminate apple leaf diseases using transfer learning

MGA-YOLO: A lightweight one-stage network for apple leaf disease detection.

Apple leaf diseases seriously damage the yield and quality of apples. Current apple leaf disease diagnosis methods primarily rely on human visual inspection, which often results in low efficiency and insufficient accuracy. Many computer vision algorithms have been proposed to diagnose apple leaf diseases, but most of them are designed to run on high-performance GPUs. This potentially limits their application in the field, in which mobile devices are expected to be used to perform computer vision-based disease diagnosis on the spot. In this paper, we propose a lightweight one-stage network, called the Mobile Ghost Attention YOLO network (MGA-YOLO), which enables real-time diagnosis of apple leaf diseases on mobile devices. We also built a dataset, called the Apple Leaf Disease Object Detection dataset (ALDOD), that contains 8,838 images of healthy and infected apple leaves with complex backgrounds, collected from existing public datasets. In our proposed model, we replaced the ordinary convolution with the Ghost module to significantly reduce the number of parameters and floating point operations (FLOPs) due to cheap operations of the Ghost module. We then constructed the Mobile Inverted Residual Bottleneck Convolution and integrated the Convolutional Block Attention Module (CBAM) into the YOLO network to improve its performance on feature extraction. Finally, an extra prediction head was added to detect extra large objects. We tested our method on the ALDOD testing set. Results showed that our method outperformed other state-of-the-art methods with the highest mAP of 89.3%, the smallest model size of only 10.34 MB and the highest frames per second (FPS) of 84.1 on the GPU server. The proposed model was also tested on a mobile phone, which achieved 12.5 FPS. In addition, by applying image augmentation techniques on the dataset, mAP of our method was further improved to 94.0%. These results suggest that our model can accurately and efficiently detect apple leaf diseases and can be used for real-time detection of apple leaf diseases on mobile devices.

On Using Deep Artificial Intelligence to Automatically Detect Apple Diseases from Leaf Images

Plant diseases, if misidentified or ignored, can drastically reduce production levels and harvest quality. Technology in the form of artificial intelligence applications has the potential to facilitate and improve the disease identification process, which in turn will empower prompt control. More specifically, the work in this paper addressed the identification of three common apple leaf diseases—rust, scab, and black rot. Twelve deep transfer learning artificial intelligence models were customized, trained, and tested with the goal of categorizing leaf images into one of the aforementioned three diseases or a healthy state. A dataset of 3171 leaf images (621 black rot, 275 rust, 630 scab, and 1645 healthy) was used. Extensive performance evaluation revealed the excellent ability of the transfer learning models to achieve high values (i.e., >99%) for F1 score, precision, recall, specificity, and accuracy. Hence, it is possible to design smartphone applications that enable farmers with poor knowledge or limited access to professional care to easily identify suspected infected plants.

A Deep-Learning Method for the Classification of Apple Varieties via Leaf Images from Different Growth Periods in Natural Environment

With the continuous innovation and development of technologies for breeding varieties of fruits, there are more than 8000 varieties of apples in existence. The accurate identification of apple varieties can promote the healthy and stable development of the global apple industry and protect the breeding property rights of rights-holders. To avoid economic losses due to the improper identification of varieties at the seedling-procurement stage, this paper proposes the classification of varieties using images of apple leaves in conjunction with the network models of traditional classification methods, supplemented with deep-learning methods, such as AlexNet, VGG, and ResNet, to account for their shortcomings in robustness and generalizability. We used the Multi-Attention Fusion Convolutional Neural Network (MAFNet) classification method for apple leaf images. The convolutional block distribution pattern of [2,2,2,2] is used to drive the feature extraction layer to have a symmetric structure. According to the characteristics of the dataset, the model is based on the ResNet model to optimize the feature extraction module and integrate a variety of attention mechanisms to achieve the weight distribution of channel features, reduce the interference information before and after feature extraction, complete the accurate extraction of image features, from low-dimensional to high-dimensional, and finally obtain the apple classification results through the Softmax function. The experiments were conducted on a mixture of leaves from 30 apple varieties at 2 growth stages: tender and mature. A total of 14,400 images were used for training, 2400 for validation, and 7200 for testing. The model’s classification accuracy was 98.14%, which improved the accuracy and reduced the classification imputation time as compared with the previous model. Among them, the accuracy rate of “Red General”, “SinanoGold”, and “Jonagold” reached 100%, and the accuracy rate of the bud variant of the Fuji line (“Fuji 2001”, “Red General”, “Yanfu 0”. and “Yanfu 3”) also had an accuracy rate of over 90%. The method proposed in this paper not only significantly improves the classification accuracy of apple cultivars, but it also achieves this with a low cost and a high efficiency level, providing a new way of thinking and an essential technical reference for apple cultivar identification by growers, operators, and law enforcement supervisors in the production practice.

Early Detection and Classification of Apple Leaf Diseases by utilizing IFPA Genetic Algorithm with MC-SVM, SVI and Deep Learning Methods

Objectives: To propose a new model for early detection and classification of apple leaf diseases by means of genetic algorithms and advanced deep learning methods in order to minimize plant degradation and maximize detection accuracy. Methods: A feature selection and extraction task is carried out by using an Improvised Flower Pollination Technique (IFPA) based genetic algorithm. Deep learning techniques such as Multi Class Support Vector Machine (MC-SVM) and Spectral Vegetation Indices (SVI) are used to classify and detect disease. The Region Growing Algorithm (RGA) and the Stochastic Gradient Descent Method (SGDM) are used to detect leaf colour segments and identify the disease at an early stage based on shape, texture, and colour features. IFPA-GA with MC-SVM and SVI extracts the features from the apple leaf dataset (collected from the Apple Experiment Station of Northwest A&F University, China) and then selects the appropriate function to perform classification and detection of disease at early occurrence and to improve the accuracy level. To demonstrate the efficiency of the proposed algorithm, MATLAB is used for implementation. The performance results are evaluated and compared to the existing models such as Faster R-CNN, R-SSD, and INARSSD. Findings: Early disease detection and classification of leaf diseases are achieved with 94.09% accuracy level, 93.07% Speed, 94.01% sensitivity, 93.38% specificity, 95.01% precision, 93.17% recall, 190 True Positive, 110 True Negative, and 92.07% F-Score to detect the disease and classify it in an optimized manner, which is high compared to the existing versions. Novelty: According to the findings of the comprehensive study, the proposed detection and classification method IFPA-GA with SVM-SVI outperforms Faster R-CNN, R-SSD, and INAR-SSD in terms of accuracy and speed of apple leaf disease detection at an early occurrence by classifying it in a robust manner. Keywords: Apple Leaf Disease Detection; SVM; Genetic Algorithm; Deep Learning; Image Processing

The removal of iron from synthetic water by the applications of plants leaf biosorbents

The toxic metals present in drinking water pose a threat to the human health. The huge populations are effected to poisonous metals mainly by the water uses. Iron is a heavy metal and found in the earth crust. The presence of iron in water might be recognized to the mixing of rocks and minerals, acid mine drainage, landfill leachate or industries waste discharges. Excessive iron ingestion can lead to an illness known as hemochromatosis. This can be the reason for liver, heart failure as well as diabetes and vomiting and skin aging etc, so that it is necessary to remove iron from water. Bio-sorption is eco-friendly method for treatment of waste water. These methods have certain advantages such as lower cost, easily available and reused. The plant leaf biosorbents have different biosorption capacities, which varied within each taxonomic group and depend on their physical and/or chemical pre-treatment as well as the operational conditions. In these research work, six locally available plant leaf biosorbents i.e. neam leaf, javapalm leaf, guava leaf, sapota leaf, custard apple leaf and mango leaf biosorbents are uses. The mango leaf and guava leaf biosorbents show that the best performance for iron removal from water. Then the optimizations are done in the various parameters like pH, contact time, biosorbent dosages, temperature and rotation speed over mango leaf biosorbents for further improvement in their performances for iron adsorption. This procedure can be made cost-effective by regenerating and reapplication of plant leaf biosorbents after removing the iron from water.

Efficacy of Botanical Extracts Against Rice Stem Borer <i>Scirpophaga Incertulas</i> (Walker)

Efficacy of botanical extracts was evaluated against the rice stem borer Scirpophaga incertulas (Wlk.) at PAJANCOA and RI, Karaikal during kharif 2019 and rabi 2019-2020. The results revealed that thiamethoxam 25WG @ 100 g/ ha reduced incidence to 4.99% and 4.13% (52.15 and 61.80% reduction, respectively). Among the botanicals, garlic and chilli extract 5% reduced this to 5.69% and 5.48%. The predatory coccinellids and spiders were more in untreated check and it was found at par with garlic and chilli extracts at 5%, followed by five leaf extracts @10% and bitter apple leaf extract 10%. The benefit cost ratio (BCR) was maximum with thiamethoxam 25WG @ 100 g/ ha (1: 1.67 and 1: 2.10), followed by garlic and chilli extract 5% (1: 1.59 and 1:1.91).

Disease detection of apple leaf with combination of color segmentation and modified DWT

In this paper, we proposed a machine learning and computer vision-based automated apple disease detection and recognition system based on leaf symptoms. The proposed method is composed of three parts: diseased region segmentation, feature extraction, and classification. We have segmented the infected portion of the leaf using L*a*b* space-based color segmentation method. Here, average color markers in a*b* space and the nearest neighbor method have been used for classifying each pixel into either healthy, infected, or background regions. We have extracted two types of features: one is the proposed DWT feature and another is L*a*b* space-based color histogram features. Horizontal feature fusion is performed to create the final feature vector. The feature vectors have been classified using several classifiers keeping Random Forrest as the base classifier. In this paper, the experiment is made on Plant Village dataset, where image of Apple Scab, Black Rot, and Cedar Apple Rust disease are taken for both training and testing our model. The fusion of proposed DWT and color histogram features is a novel approach in detecting and recognizing apple leaf disease, which got an accuracy of 98.63%.

Apple Leaf Diseases Classification Using CNN with Transfer Learning

Abstract: The Foliar diseases of the apple trees commonly reduce the crop Yield and photosynthesis which affects their productivity. Diagnosing foliar damage is not easy if there are no distinct patterns that would be fungal fruiting bodies it will. spread to the rest of the crops. The foliar disease of the apple trees is carried out due to biotic and abiotic causes, some of the biotic causes of foliar damage are - Bacterial Disease, Fungal Diseases, Viral Diseases, Insects, and Mites That Damage Foliage. some of the Abiotic causes are - Iron Chlorosis, Misapplied Herbicide, and Winter Desiccation of Evergreens. Traditional approaches rely on visual inspection by an expert and biological examination is the second choice .these approaches are time-consuming and expensive. we use machine learning methods to classify the disease in apple trees. we use some pre-trained CNN models to extract features from the dataset, we applied the CNN model and compared them with Pre-trained Models, and we achieve accuracies of over 93% with CNN, among the Models We achieved 92% with the Inception V3 model,62% with VGG16, 63% with VGG19.

The Biorevitalization Effect of Serum Containing Rose Apple Leaf Extract on the Parameters of Premature Skin Aging

Rose apple (Syzygium jambos) is known for its antioxidants and could potentially be used as an anti-aging cosmetic. This study reported the potential of rose apple leaf extract as a skin serum for the biorevitalization- with improvement based on pigmentation, wrinkles, pore, and sensitivity parameters in the skin of women aged 30 to 40. The skin serum was formulated with various concentrations of 5%, 10%, and 15% of rose apple leaf extract. The Pretest-Posttest with Control Group Design were conducted by measuring skin condition with the API-100 Aram Huvis Innovation Skin Analysis System. Data analyses using the Kruskal-Wallis’s test and Mann-Whitney’s test. The phytochemical contents of rose apple leaf extracts showed alkaloids, flavonoids, saponins, phenol as gallic acid, and tannic acid, which function as antioxidants that is comparable to vitamin C with IC50 of up to 7.29%. Experimental data showed that rose apple leaf extract affects skin biorevitalization based on reduction in pore size, skin pigmentation, and sensitivity parameters. This study strongly suggests the potential of rose apple leaf extract as an anti-aging ingredient to prevent premature skin aging.

Fruit trees rust: genus Gymnosporangium pathology spread and host plants of the pathogens

Phytopathological examination of orchards of various forms of ownership reveal that every year the rust of pip fruit trees has become widespread and reached its epiphytosis. In 2008, we detected symptoms of rust on pip fruit trees in the form of single yellow spots on pear leaves in orchards of different ages and forms of ownership. The identifcation found that the fungus Gymnosporangium sabinae G. Winter, (1882) (syn. G. fuscum DC. (1805)) was the causative agent of pear rust. The prevalence of pathology was insignifcant within 3–5 %. Every year the percentage of the pathology spread and development had been increasing and in 2021 it amounted to 55-75% and 4-5 points, respectively. Symptoms of the lesion were observed on leaves, young shoots and fruits. Microscopic studies found several species of pathogens of the genus Gymnosporangium, namely G. sabinae G. Winter, G. dobrozrakovae Mitrof., G. confusum Plowr., dominated by G. sabinae G. Winter that in the pathological complex of the affected organs of the pear there are. In 2012, we detected symptoms of rust on apple tree leaves in the form of single yellow spots, with a prevalence of 3 %. The identifcation established that the fungi G. tremelloides Hartig and G. dobrozrakovae Mitrof. were the causative agents of apple leaf rust are with G. tremelloides Hartig having a signifcant dominance. In 2021, the percentage of prevalence and development of this pathology ranged 25–30 % and 2–3 points, respectively. In the collection fund of the Botanical Garden of BNAU the section of Angiosperms is represented by one genus Cydonia Mill and the species C. oblonga Mill. In 2018, we noted a few small spots on the leaves of quince caused by the pathogen G. confusum Plowr. The microscopic analysis of the affected organs of species of the genus Juniperus L. with the signs of rust reveal that the pathological complex involved four pathogens of the genus Gymnosporangium, which are the causative agents of pip fruit trees rust. Seven species of Juniperus L. are ecological niches for rust pathogens of pear, apple and quince, namely Juniperus communis L. – for G. tremelloides Hartig, Juniperus excels – for G. sabinae Wint., G. dobrozrakovae Mitrof., J.foetidissima – G. sabinae Wint. G. dobrozrakovae Mitrof., G. confusum Plowr., J. oxycedrus – for G. sabinae Wint., G. confusum Plowr., J. phoenicea – for G. sabinae Wint., G. confusum Plowr., J. sabina - G. sabinae Wint., J. macropoda – G. confusum Plowr. Кey words: pip fruit trees, rust, agent, Gymnosporangium, Juniperus L.

Improving the Estimation of Apple Leaf Photosynthetic Pigment Content Using Fractional Derivatives and Machine Learning

As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R2 = 0.78) and 0.4 (R2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC.

Transformer Help CNN See Better: A Lightweight Hybrid Apple Disease Identification Model Based on Transformers

The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the above problems, this paper proposes a Vision Transformer-based lightweight apple leaf disease- identification model, ConvViT, to extract effective features of crop disease spots to identify crop diseases. Our ConvViT includes convolutional structures and Transformer structures; the convolutional structure is used to extract the global features of the image, and the Transformer structure is used to obtain the local features of the disease region to help the CNN see better. The patch embedding method is improved to retain more edge information of the image and promote the information exchange between patches in the Transformer. The parameters and FLOPs (Floating Point Operations) of the model are significantly reduced by using depthwise separable convolution and linear-complexity multi-head attention operations. Experimental results on a complex background of a self-built apple leaf disease dataset show that ConvViT achieves comparable identification results (96.85%) with the current performance of the state-of-the-art Swin-Tiny. The parameters and FLOPs are only 32.7% and 21.7% of Swin-Tiny, and significantly ahead of MobilenetV3, Efficientnet-b0, and other models, which indicates that the proposed model is indeed an effective disease-identification model with practical application value.

No adverse dietary effect of a cisgenic fire blight resistant apple line on the non-target arthropods Drosophila melanogaster and Folsomia candida

Genetic modification of apple cultivars through cisgenesis can introduce traits, such as disease resistance from wild relatives, quickly and without crossing. This approach was used to generate the cisgenic apple line C44.4.146, a 'Gala Galaxy' carrying the fire blight resistance gene FB_MR5. In contrast to traditionally bred apple cultivars, genetically modified (GM) plants need to undergo a regulatory risk assessment considering unintended effects before approval for commercial release. To determine potential unintended effects of C44.4.146, we assessed major leaf components and effects on the fitness of the decomposers Drosophila melanogaster (fruit fly) and Folsomia candida (collembolan), which were fed a diet amended with powdered apple leaf material. Leaf material of 'Gala Galaxy', several natural 'Gala' mutants, and the unrelated apple cultivar 'Ladina' were used for comparison. The genetic modification did not alter major leaf components and did not adversely affect survival, growth, or fecundity of the two decomposers. Consistent with previous studies with other GM crops, the differences between conventionally bred cultivars were greater than between the GM line and its non-GM wild type. These data provide a baseline for future risk assessments.

Apple Leaf Disease Classification Using Image Dataset: a Multilayer Convolutional Neural Network Approach

Agriculture is one of the prime sources of economic growth in Russia; the global apple production in 2019 was 87 million tons. Apple leaf diseases are the main reason for annual decreases in apple production, which creates huge economic losses. Automated methods for detecting apple leaf diseases are beneficial in reducing the laborious work of monitoring apple gardens and early detection of disease symptoms. This article proposes a multilayer convolutional neural network (MCNN), which is able to classify apple leaves into one of the following categories: apple scab, black rot, and apple cedar rust diseases using a newly created dataset. In this method, we used affine transformation and perspective transformation techniques to increase the size of the dataset. After that, OpenCV crop and histogram equalization method-based preprocessing operations were used to improve the proposed image dataset. The experimental results show that the system achieves 98.40% training accuracy and 98.47% validation accuracy on the proposed image dataset with a smaller number of training parameters. The results envisage a higher classification accuracy of the proposed MCNN model when compared with the other well-known state-of-the-art approaches. This proposed model can be used to detect and classify other types of apple diseases from different image datasets.

Comparative efficacy of bio-agents and botanical extracts against Helicoverpa armigera (Hubner) on chickpea (Cicer arietinum l.) crop

Efficacy of bio-agents and botanical extracts against H. armigera was assessed in the field during the 2020-2021 Rabi Season, with three replications of the Randomize Block Design. When compared to the control, all of the bio-agents and botanical extracts tested were found to be effective in suppressing the gram pod borer infestation. Beauveria bassiana was shown to be the most effective in reducing H. armigera 14.18 percent pod damage, followed by Metarhizium anisopliae -15.37 percent pod damage, among all the spray materials studied. Pod infestation was lowest in the Beauveria bassiana test plots and highest in the control plots. Similarly, the Beauveria bassiana treated plot yielded the most (14.48 q/ha), while the control plot produced the least (8. q/ha). Beauveria bassiana was found to be the most effective in controlling Gram pod borer infestation, resulting in the least amount of pod damage and thus increased Chickpea yield. However, the Chemical and synthetic insecticide was extremely toxic. Furthermore, Metarhizium anisopliae, EPN, Custard apple leaf extract, Sweet flag rhizome oil and clove oil extract also showed better results than control in term of pest reduction and were comparatively safe to the environment. Hence these botanicals and bio-agents should be included in IPM program for the suitable management of H. armigera associated with Chickpea crop.

Deep diagnosis: A real-time apple leaf disease detection system based on deep learning

Background and objectiveDiseases and pests are one of the major reasons for low productivity of apples which in turn results in huge economic loss to the apple industry every year. Early detection of apple diseases can help in controlling the spread of infections and ensure better productivity. However, early diagnosis and identification of diseases is challenging due to many factors like, presence of multiple symptoms on same leaf, non-homogeneous background, differences in leaf colour due to age of infected cells, varying disease spot sizes etc. MethodsIn this study, we first constructed an expert-annotated apple disease dataset of suitable size consisting around 9000 high quality RGB images covering all the main foliar diseases and symptoms. Next, we propose a deep learning based apple disease detection system which can efficiently and accurately identify the symptoms. The proposed system works in two stages, first stage is a tailor-made light weight classification model which classifies the input images into diseased, healthy or damaged categories and the second stage (detection stage) processing starts only if any disease is detected in first stage. Detection stage performs the actual detection and localization of each symptom from diseased leaf images. ResultsThe proposed approach obtained encouraging results, reaching around 88% of classification accuracy and our best detection model achieved mAP of 42%. The preliminary results of this study look promising even on small or tiny spots. The qualitative results validate that the proposed system is effective in detecting various types of apple diseases and can be used as a practical tool by farmers and apple growers to aid them in diagnosis, quantification and follow-up of infections. Furthermore, in future, the work can be extended to other fruits and vegetables as well.