Diseased Tomato Plants Research Articles

Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Automated plant disease identification is one of the significant tasks in the agricultural sector. With the advent of deep-learning models, classification and segmentation have been completely automated using a convolutional neural network (CNN). This paper proposes the UV-Net model, with a recurrent encoder and an up-sampling decoder for multiclass image segmentation and generation of masks. The improvement of the segmentation model is the stacking of conv2D layers before the down convolution operation, concatenation of features extracted from the convolution process, up-sampling along with transpose convolution in the expansion path, and exponential linear unit (ELU) activation. This model is combined with an optimized, pretrained, and self-trained CNN for generating the classes of diseased leaves. The classification is further enhanced by concatenating the high-level features extracted from the pretrained CNN model along with the low-level features from the encoder part of the UV-Net model. The features are concatenated using the hybrid wind-driven particle swarm optimized (WDPSO) deep feature selective concatenation mechanism (DFSCM). The proposed model is trained and the performance is tested on multiclass diseases of tomato plants like bacterial spot, early blight, leaf mold, and target spot. The proposed architecture is also compared with some of the well-known CNN models such as FCN, FPN, U-Net, V-Net, and Mask R-CNN. The model proves to be a robust system for detecting diseases from images of varying sizes, shapes, and lighting. The proposed model achieved an average training accuracy of 98.20% with a dice coefficient of 0.8938 and an Intersection over union coefficient (IoU) of 0.7458. The model also generates accurate masks from which the intensity and pattern of diseases could be identified for earlier prevention and treatment of diseases.

Sistem Pakar Diagnosa Hama Penyakit Tanaman Tomat Menggunakan Metode Forward Chaining

The tomato (Solanum lyco persicum) is a plant from the solanceae family, native to Central and South America, from Mexico and Peru. Tomatoes have a short life cycle and height from I to 3 meters. Currently, farmers often ignore pests and diseases of tomatoes due to lack of knowledge. They also assessed that it finally happened at the time of planting, experts on pests and diseases of tomatoes were eliminated, there was a lack of socialization from agricultural officials about pests and diseases in tomatoes, but they did not know how to control them and to eliminate diseases quickly or decrease productivity. This study uses the Forward Chaining method, which is a description that starts from known facts and then is matched with IF and IF-THEN rules. Pests and diseases with a high prevalence in tomatoes are pests. Some pests are: fruit caterpillars, earthworms, green aphids, white flies, fruit flies (bractrotera) and further diseases namely: Fusarium wilt, leaf spot disease, bacterial spots, fruit rot. This expert system application provides information on tomato diseases and disease diagnosis, including control solutions that can be used to reduce the risk of damage to tomato plants. This study aims to build an expert system application that can diagnose tomato plant pests and diseases using the forward chaining method. This application is expected to help the community in diagnosing the types of tomato plant pests. The implementation of the application of an expert system for pests and diseases of tomato plants is expected to provide easy access for users and the wider community, through the use of web-based media.

Comparative analysis of Penicillium genomes reveals the absence of a specific genetic basis for biocontrol in Penicillium rubens strain 212.

Penicillium rubens strain 212 (PO212) is a filamentous fungus belonging to the division Ascomycete. PO212 acts as an effective biocontrol agent against several pathogens in a variety of horticultural crops including Fusarium oxysporum f.sp. lycopersici, causing vascular wilt disease in tomato plants. We assembled draft genomes of two P. rubens strains, the biocontrol agent PO212 and the soil isolate S27, which lacks biocontrol activity. We also performed comparative analyses of the genomic sequence of PO212 with that of the other P. rubens and P. chrysogenum strains. This is the first Penicillium strain with biocontrol activity whose genome has been sequenced and compared. PO212 genome size is 2,982 Mb, which is currently organized into 65 scaffolds and a total of 10,164 predicted Open Reading Frames (ORFs). Sequencing confirmed that PO212 belongs to P. rubens clade. The comparative analysis of the PO212 genome with the genomes of other P. rubens and Penicillium chrysogenum strains available in databases showed strong conservation among genomes, but a correlation was not found between these genomic data and the biocontrol phenotype displayed by PO212. Finally, the comparative analysis between PO212 and S27 genomes showed high sequence conservation and a low number of variations mainly located in ORF regions. These differences found in coding regions between PO212 and S27 genomes can explain neither the biocontrol activity of PO212 nor the absence of such activity in S27, opening a possible avenue toward transcriptomic and epigenetic studies that may shed light on this mechanism for fighting plant diseases caused by fungal pathogens. The genome sequences described in this study provide a useful novel resource for future research into the biology, ecology, and evolution of biological control agents.

Performance evaluation of ResNet model for classification of tomato plant disease

Abstract Objectives The plant tomato (Solanum Lycopersicum) is vastly infected by various diseases. Exact diagnosis on time contributes a significant job to the good production of tomato crops. The key objective of this article is to recognize the infection in tomato leaves with better accuracy and in less time. Methods Nowadays deep convolutional neural networks have attained surprising outcomes in several applications, together with the categorization of tomato leaves infected with several diseases. Our work is based on deep CNN with different residual networks. Finally; we have performed tomato leaves disease classification by using pre-trained deep CNN with the residual network using MATLAB available on the cloud. Results We have used a dataset of tomato leaves for the experiments which contain six different types of diseases with one healthy tomato leaf class. We have collected 6,594 tomato leaves dataset from Plant Village and we did not collect actual tomato leaves for testing. The outcome obtained by ResNet-50 shows a significant result with 96.35% accuracy for 50% training and 50% testing data and if we focus on time consumption for the outcome then ResNet-18 consumes 12.46 min for 70% training and 30% testing. Conclusions After observation of several outcomes, we have concluded that ResNet-50 shows a better accuracy for 50% training and 50% testing of data and ResNet-18 shows better efficiency for 70% training and 30% testing of data for the same dataset on the cloud.

Non-coding deep learning models for tomato biotic and abiotic stress classification using microscopic images.

Plant disease classification is quite complex and, in most cases, requires trained plant pathologists and sophisticated labs to accurately determine the cause. Our group for the first time used microscopic images (×30) of tomato plant diseases, for which representative plant samples were diagnostically validated to classify disease symptoms using non-coding deep learning platforms (NCDL). The mean F1 scores (SD) of the NCDL platforms were 98.5 (1.6) for Amazon Rekognition Custom Label, 93.9 (2.5) for Clarifai, 91.6 (3.9) for Teachable Machine, 95.0 (1.9) for Google AutoML Vision, and 97.5 (2.7) for Microsoft Azure Custom Vision. The accuracy of the NCDL platform for Amazon Rekognition Custom Label was 99.8% (0.2), for Clarifai 98.7% (0.5), for Teachable Machine 98.3% (0.4), for Google AutoML Vision 98.9% (0.6), and for Apple CreateML 87.3 (4.3). Upon external validation, the model's accuracy of the tested NCDL platforms dropped no more than 7%. The potential future use for these models includes the development of mobile- and web-based applications for the classification of plant diseases and integration with a disease management advisory system. The NCDL models also have the potential to improve the early triage of symptomatic plant samples into classes that may save time in diagnostic lab sample processing.

Machine Learning Based Approach in High-Performance of Deep Transfer Learning Model with Batch Normalization Method for Tomato Plant Disease Identification and Categorization

Machine Learning Based Approach in High-Performance of Deep Transfer Learning Model with Batch Normalization Method for Tomato Plant Disease Identification and Categorization

Advanced Plant Disease Detection Using Neural Network

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of tomato plant disease recognition model, based on leaf image classification, by the use of deep convolutionalnetworks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize different types of tomato plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the project, starting from gathering images in order to create a database, assessed by agricultural experts. Neural network, was used to perform the disease detection. The experimental results on the developed model achieved detection between 85% and 95%.

Efficiency of some Organic Matter on Suppression of Soil-Borne Diseases of Tomato Plants under Organic Agriculture System

Efficiency of some Organic Matter on Suppression of Soil-Borne Diseases of Tomato Plants under Organic Agriculture System

Effect of foliar-applied Euphorbia hirta towards controlling bacterial diseases in tomato crops and enhancing fruit yield and shelf life

ABSTRACT Bacterial diseases are a major cause of yield loss in tomato plants worldwide, and there is a need to identify strategies for control of these diseases. Bacterial wilt caused by Ralstonia solanacearum (RS) and bacterial spot caused by Xanthomonas axonopodis pv vesicatoria (Xav) are amongst the bacterial diseases affecting tomato crops. The current study investigates Euphorbia hirta (E. hirta) methanol extract (EHM) for its antibacterial activity against RS, and Xav induced diseases in tomato plants, along with its biostimulant potential in increasing fruit yield. EHM at 1280 mg/L exhibited 90% inhibition in the growth of both RS and Xav bacteria. Further, evaluation of EHM on Arka Vikas, a susceptible tomato variety, showed that pre-treatment of 15 days and 45 days tomato plants with 1280 mg/L EHM exhibited moderate resistance to bacterial wilt and spot disease, respectively, with increased tomato yield and improved chlorophyll b levels compared to control plants. The effectiveness of EHM in increasing fruit yield was further confirmed using field evaluation on ‘Namdhari NS 538’ variety of tomato plants. A 3.5-fold increase in numbers and weight of tomato was observed with foliar application of EHM (1280 mg/L). A significant reduction in fruit spoilage along with an increase in mRNA level of ethylene receptor factor (ERF1) was also observed. The study is the first to demonstrate the cost-effective and eco-friendly antibacterial and biostimulant potential of EHM (1280 mg/L) for tomato plants.

Multi-Class Plant Leaf Disease Detection Using a Deep Convolutional Neural Network

Traditional machine learning methods of plant leaf disease detection lack successful performances due to poor feature representation and correlation. This paper presents a novel methodology for automatic plant leaf disease detection using cascaded deep convolutional neural network (CDCNN) which focusses on increasing the feature representation and correlation factors. It provides distinctive features that gives low intra-class variability and higher inter-class variability. CDCNN were performed on a plant-village leaf disease database which consists of 13 classes of tomato, potato, and pepper bell plant diseases; DCNN model performs better with an overall accuracy, recall, and precision of 98.50%, 0.98, and 0.97 respectively. Additionally, performance of the proposed algorithm is evaluated on real time cotton leaf database for bacterial blight, leaf miner, and spider mite diseases detection and provides 99.00% accuracy. The proposed DCNN outperforms well compared to traditional machine learning and deep learning models and is able to detect the diseases present in the leaves of the plant.

Expert System for Diagnosing Diseases in Tomato Plants Using a Website-Based Forward Chaining Method

Tomato plant is one of the plants in agriculture that is widely cultivated in Indonesia, one of which is in East Sumba Regency. The problem that is often faced is the lack of knowledge and facilities for tomato farmers which are still limited in dealing with diseases in these tomatoes. To diagnose tomato plant diseases that attack through the symptoms they cause, a tomato plant expert is needed to analyze and diagnose. Currently farmers often ignore tomato diseases due to lack of knowledge. They assume it's finally happening in the planting phase, tomato pest and disease experts are reduced, they don't know how to control and eliminate decreased productivity. In this case, to help farmers know the type of disease that attacks, an expert system is needed to help diagnose tomato plant diseases and provide the right solution in handling the disease using the Forward Chaining method. In the Forward Chaining method, the data search process starts from the premise to the final conclusion. Based on the test results, it can be seen that the probability value for the accuracy of the expert system produced is capable of having a percentage of 100%, from the calculation of the 8 types of the same disease, for inaccuracy so that this expert system can be stated to have been running well.

Classification of Tomato Plant Diseases using Deep Learning Based Classifier from Images of Leaves

Crop diseases cost farmers a lot of money every year in the majority of agricultural nations. Therefore, early detection of tomato plant diseases has become a major research focus. In this study, a deep convolutional neural network model is utilised to distinguish between diseased and healthy plants and to categorise the diseases that affect tomato plants. To identify ill plants and their diseases from photos of tomato plants, we employed the VGG16 deep CNN classifier. We used the Plant Village dataset, which includes healthy plants and ten different classes of tomato leaf photos. With a pre-trained VGG16 model and the transfer learning approach, this dataset exhibits a satisfying classification performance of roughly 95.5%. This model's top 2 accuracy for identifying illnesses in tomato plants is 99%. Our trained model exhibits a performance of about 100% to distinguish ill plants from healthy plants without utilising any segmentation or pre-processing of leaf pictures.

Smart farming for detection and identification of tomato plant diseases using light weight deep neural network

Smart farming for detection and identification of tomato plant diseases using light weight deep neural network

Diagnosis of Tomato Plant Diseases Using Pre-Trained Architectures and A Proposed Convolutional Neural Network Model

Tomato is one of the most important vegetables in the world. Presence of diseases and pests in the growing area significantly affect the choice of variety in tomato. Early-stage diagnosis plays an important role in determining whether the tomato is subject to effective and economical chemical, mechanical and biological controls, and internal and external quarantine. In this study, deep learning was used to diagnose some diseases in tomatoes. For this purpose, a novel deep CNN-based approach and some Keras models including DenseNet201, InceptionResNetV2, MobileNet, VGG16 architectures were used. Early, middle, and late stages of 18.456 images of Bacterial Spot, Early Blight, Leaf Mold, Septoria Leaf Spot, Target Spot, Mosaic Virus, Yellow Leaf Curl Virus and healthy leaves were examined. The experimental results showed that the custom CNN model produced 99.82% training accuracy. We recommend this model in terms of monitoring and diagnosing of tomato diseases. The results obtained with this study can be used in robotic spraying and harvesting operations.

Tomato and Jute Mallow are Two New Hosts of Papaya Bunchy Top Phytoplasma, a 16SrXII-O Subgroup Strain in Nigeria.

Nigerian papaya bunchy top (NGPBT) phytoplasma was first identified in diseased papaya plants growing in Ibadan, Oyo State, Nigeria (Kazeem et al. 2021). The NGPBT phytoplasma is a 'Candidatus Phytoplasma convolvuli'-related strain and represents a subgroup lineage, 16SrXII-O (the accession number of the reference strain is MW530522, Kazeem et al. 2021). The present communication reports that NGPBT phytoplasma can also infect tomato (Solanum lycopersicum) and jute mallow (Corchorus olitorius). Since May 2020, tomato and jute mallow grwn in Ibadan have been observed to develop yellowing, little leaf, and stunting symptoms (Fig. 1). Because the symptomatic plants occurred in the region approximately 1 km adjacent to where the NGPBT disease was reported, and the symptoms of infected plants resembled those of phytoplasma infection, molecular diagnostic assays for phytoplasma detection were deployed. Total DNAs were extracted from symptomatic plants, including four tomato plants and three jute mallows, as well as from asymptomatic two tomato and two jute mallow plants. The DNA samples were subjected to semi-nested PCR using phytoplasma 16S rRNA gene-specific primers P1A and P7A, followed by P1A and 16S-SR (Lee et al. 2004). An amplicon of 1.5 kb was obtained from each of the symptomatic plants, while no amplicon resulted from DNA samples of asymptomatic plants or negative controls without DNA templates (water and PCR reagents only). PCR products were cloned into the TOPO TA cloning vector (Invitrogen, Carlsbad, CA, USA), and three clones were chosen for each sample for Sanger sequencing (Psomagen Inc., Rockville, MD, USA). The nearly full-length 16S rRNA gene sequences (1.53kb) derived from tomato (OP123558) and jute mallow (OP123559) samples were identical. Based on the iPhyClassifier phytoplasma classification web tool (Zhao et al. 2009) and the BLAST search against the NCBI nucleotide database, these phytoplasma strains showed 100% sequence identity in 16S rRNA gene with the NGPBT phytoplasma (16SrXII-O, MW530522). Moreover, two additional genetic loci including ribosomal protein genes rplV-rpsC, and rplO-secY-adk were also amplified by nested PCR or semi-nested PCR with specific primers rpStolF/rpStolR followed by rpStolF2/rpStolR (Martini et al. 2007), and SecYF1a (Xll)/MapR-703-a, followed by SecYF2a (Xll)/MapR-703-a (Lee et al. 2010). Gene fragments of rplV-rpsC (1238bp) and rplO-secY-adk (2064bp) were amplified from DNAs of diseased papaya, tomato, and jute mallow plants. The obtained sequences were deposited into GenBank, respectively: rplV-rpsC (OP123560, OP123562, and OP123563) and rplO-secY-adk (OP123565, OP123567, and OP123568). Multilocus sequence analysis (MLSA) indicated that the sequences of phytoplasmas amplified from three different plant hosts were also identical in rp, secY, and adk genes. The MLSA results demonstrate that tomato and jute mallow are two new hosts of NGPBT phytoplasmas. This also marks the first time that phytoplasma diseases are associated with tomato and jute mallow in Nigeria, as prior to this study, phytoplasma diseases were only reported in coconut palm and papaya in the country (Osagie et al. 2016; Kazeem et al. 2021). Results from the present study suggest that insect vector(s) for the transmission of the NGPBT phytoplasma are present in the region. Since both tomato and jute mallow are important vegetable crops in Nigeria, timely dissemination of emerging disease information is needed to alert growers and extension personnel in the region. In addition, ongoing incidence, and prevalence surveys of NGPBT disease indicate that more infected papaya and tomato plants have been observed in the region than in previous years. A better understanding of the NGPBT phytoplasma disease epidemiology will help devise strategies to control the diseases associated with the NGPBT phytoplasma.

Expression of defense genes and free radical scavenging on pre supplementation of silicon to Alternaria solani inoculated Lycopersicon esculentum plants

Biotic stress on Lycopersicon esculentum laden by the various biotrophic and necrotrophic fungal pathogens forms the most devastating diseases of tomato plants in India. In this study, the expression of defense-related genes was carefully evaluated in silicon (Si) treated and Alternaria solani inoculated plants by using reverse transcription polymerase chain reaction (RT-PCR) and real time quantitative polymerase chain reaction (qRTPCR). We also determined the effect of Si on disease prevention and free radical scavenging potential. Based on the outcomes, it was found that Si has a tremendous impact on expression of ACCO, TSRF1, POD and CHI II in Si + A. solani inoculated plants than control and plants that were only inoculated with A. solani. However, ACCO gene showed less expression in Si treated condition than other genes. Furthermore, prior addition of Si diminishes the levels of ROS and increases the scavenging activity of free radical as DPPH, Hydroxyl, Superoxide anion and hydrogen peroxide in Lycopersicon esculentum. Altogether, In response to A. solani, Si not only increases the transcription levels of defence signature genes, but also the transcription levels of other genes and also free radical scavenging potential in tomato plants was enhanced.

A robust deep learning approach for tomato plant leaf disease localization and classification

Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however, the timely localization and identification of various tomato leaf diseases is a complex job as a consequence of the huge similarity among the healthy and affected portion of plant leaves. Furthermore, the low contrast information between the background and foreground of the suspected sample has further complicated the plant leaf disease detection process. To deal with the aforementioned challenges, we have presented a robust deep learning (DL)-based approach namely ResNet-34-based Faster-RCNN for tomato plant leaf disease classification. The proposed method includes three basic steps. Firstly, we generate the annotations of the suspected images to specify the region of interest (RoI). In the next step, we have introduced ResNet-34 along with Convolutional Block Attention Module (CBAM) as a feature extractor module of Faster-RCNN to extract the deep key points. Finally, the calculated features are utilized for the Faster-RCNN model training to locate and categorize the numerous tomato plant leaf anomalies. We tested the presented work on an accessible standard database, the PlantVillage Kaggle dataset. More specifically, we have obtained the mAP and accuracy values of 0.981, and 99.97% respectively along with the test time of 0.23 s. Both qualitative and quantitative results confirm that the presented solution is robust to the detection of plant leaf disease and can replace the manual systems. Moreover, the proposed method shows a low-cost solution to tomato leaf disease classification which is robust to several image transformations like the variations in the size, color, and orientation of the leaf diseased portion. Furthermore, the framework can locate the affected plant leaves under the occurrence of blurring, noise, chrominance, and brightness variations. We have confirmed through the reported results that our approach is robust to several tomato leaf diseases classification under the varying image capturing conditions. In the future, we plan to extend our approach to apply it to other parts of plants as well.

EXPERT SYSTEM FOR DIAGNOSING TOMATO PESTS AND DISEASES USING FORWARD CHAINING METHOD

Tomato (solanum lyco persicum) is a plant from the solanceae family, native to Central and South America, from Mexico and Peru. Tomatoes have a short life cycle and a height ranging from 1 to 3 meters. Currently, farmers often experience disturbances from pests and diseases of tomatoes due to a lack of knowledge. They do not know how to control it and to eliminate pests that reduce productivity. In identifying diseases in tomato plants we use the Forward Chaining method which is a description that starts from knowing the facts and then matching them with IF and IF-THEN rules. Diseases with a high prevalence in tomatoes are: fruit worms, earthworms, green aphids, white flies, fruit flies (bractrotera) and the diseases are: fusarium wilt, leaf spot disease, bacterial spot, fruit rot. This expert system application provides tomato disease information and disease diagnosis, including control solutions that can be used to reduce the risk of tomato crop damage
 

RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN.

In today’s era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops. Normally, in tomato plants, 40–60% may be damaged due to leaf diseases in the field if the cultivators do not focus on control measures. In tomato production, these diseases can bring a great loss. Therefore, a proper mechanism is needed for the detection of these problems. Different techniques were proposed by researchers for detecting these plant diseases and these mechanisms are vector machines, artificial neural networks, and Convolutional Neural Network (CNN) models. In earlier times, a technique was used for detecting diseases called the benchmark feature extraction technique. In this area of study for detecting tomato plant diseases, another model was proposed, which was known as the real-time faster region convolutional neural network (RTF-RCNN) model, using both images and real-time video streaming. For the RTF-RCNN, we used different parameters like precision, accuracy, and recall while comparing them with the Alex net and CNN models. Hence the final result shows that the accuracy of the proposed RTF-RCNN is 97.42%, which is higher than the rate of the Alex net and CNN models, which were respectively 96.32% and 92.21%.

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease.

Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images—using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.