Identification Of Plant Diseases Research Articles

Processing Plant Diseases Using Transformer Model

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.

Efficient plant disease identification using few-shot learning: a transfer learning approach

Efficient plant disease identification using few-shot learning: a transfer learning approach

Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh

Bangladesh is heavily reliant on rice production, but a staggering annual decline of 37% in rice output due to insufficient knowledge in recognizing and managing rice plant diseases has raised concerns. As a result, there is a pressing need for a system that can accurately identify and control rice plant diseases automatically. CNNs have demonstrated their effectiveness in detecting plant diseases, thanks to their exceptional image classification capabilities. Nevertheless, research on rice plant disease identification remains scarce. This study offers a comprehensive overview of rice plant ailments and explores DL techniques used for their detection. By evaluating the advantages and disadvantages of various systems found in the literature, the study aims to identify the most accurate means of detecting and controlling rice plant diseases using DL techniques. We present a real-time detection and diagnostic system for rice lead diseases that utilizes ML methods. This system is designed to identify three prevalent rice plant diseases, specially leaf smut, bacterial leaf blight and brown spot diseases. Clear images of affected rice leaves against a white background serve as input data for the system. To train the dataset, several ML algorithms were employed including KNN, Naive Bayes, J48 and Logistic Regression. Following the pre-processing stage, the decision tree algorithm demonstrated an accurateness of over 97% when claimed to test dataset. In conclusion, implementing an automated system that leverages ML techniques is vital for reducing the time and labor required for detecting and managing rice plant diseases. Such a system would contribute significantly to ensuring the healthy growth of rice plants in Bangladesh, ultimately boosting the nation’s rice production.

Deteksi Penyakit Pada Daun Tanaman Ubi Jalar Menggunakan Metode Convolutional Neural Network

Sweet potatoes are the world's third most important root crop and the fourth most popular staple food in developing countries, including Indonesia. Some diseases commonly found in sweet potato leaves are early blight (identified by leaves containing batataezim) and late blight (characterized by leaves that have chlorosis). These two diseases have different symptoms and require different treatments, but a slow identification process can lead to additional costs for plant care. This research will classify image data of sweet potato diseases using the Convolutional Neural Network (CNN) method. CNN is a derivative of the Multilayer Perceptron (MLP) designed to process image data with high network depth and is often used for classification tasks. The research uses a total of 750 images divided into 3 classes: images of healthy leaves, images of leaves with chlorosis, and images of leaves containing batataezim. Each leaf class will be labeled with 250 image data, and the labeled data will be further divided into training and testing sets. From these sets, prediction data will be obtained from the testing process during the CNN model training. The training accuracy resulted in a value of 98.17%, while the testing accuracy reached 98.67%. Additionally, the resulting loss values are remarkably low, at 0.04% for training and 0.03% for testing. The research findings will provide insights into the CNN method's ability to detect diseases in sweet potato plants, potentially impacting agricultural supervision, plant disease identification, and enabling more precise decisions regarding plant care actions.

Evolutionary meta-heuristic optimized model: An application to plant disease diagnosis

Deep learning strategies have been achieved over the historical decades to resolve many computer vision applications. Recently, these deep learning algorithms have been extensively used as a tool in classification problems. Generally, the deep learning algorithms trained with gradient-based optimizers, which has some downsides such as the slow speed of convergence and stuck in local minima. As a solution, the planned work using meta-heuristic based Grey Wolf and Whales optimization algorithms for the automatic plant disease detection model. The planned work has explored the application of automatic plant disease identification through the leaf images with the help of the image processing approach. The planned research has evaluated the deep learning algorithm with Grey Wolf and Whales optimization techniques using the three types of datasets, such as Plant Village, New Plant Disease, and Rice Leaf Disease databases. The simulation consequences illustrate that the computational efficiency of the Grey Wolf and Whales based automatic disease identification process is boosted when coupled with the deep learning method.

Digital image processing applications in agriculture with a machine learning approach

Abstract. Digital image processing involves the manipulation of images by digital means, and its use has been increasing exponentially in recent decades. It is applied in a diverse range of fields including medicine, remote sensing, robotic vision, and audiovisual processing. Image processing technologies are widely used as a proficient tool in the agriculture sector. The combination of digital image processing and machine learning not only provides new insights into crop health and environmental circumstances, but also helps farmers reach timely and precise decisions. Using machine learning methods, this study examines digital image processing applications in agriculture, focusing on plant disease identification, fruit quality evaluation, weed detection, yield mapping, and robotic harvesting, to other issues. In essence, this study integrates existing knowledge at the interface of digital image processing, machine learning, and agriculture, providing insights into a promising and growing topic with significant implications for sustainable and resilient food production.

A deep learning-based mobile app system for visual identification of tomato plant disease

<span lang="EN-US">Tomato is one of many horticulture crops in Indonesia which plays a vital role in supplying public food needs. However, tomato is a very susceptible plant to pests and diseases caused by bacteria and fungus. The infected diseases should be isolated as soon as it was detected. Therefore, developing a reliable and fast system is essential for controlling tomato pests and diseases. The deep learning-based application can help to speed up the identification of tomato disease as it can perform direct identification from the image. In this research, EfficientNetB0 was implemented to perform multi-class tomato plant disease classification. The model was then deployed to an android-based application using machine learning (ML) kit library. The proposed system obtained satisfactory results, reaching an average accuracy of 91.4%.</span>

High-performance deep transfer learning model with batch normalization based on multiscale feature fusion for tomato plant disease identification and categorization

Deep learning and machine learning are cutting-edge methods for analysing images that have considerable potential. Artificial Neural Networks (A-NNs), one of the most well-known methods of computer intelligence, are now used in machine learning (ML) and deep transfer learning (DL) to raise plant production and quality. Identification and primary prevention of plant diseases at the appropriate time are essential for boosting productivity. Due to the phenomenon of minimally intense data in the background and foreground areas of the image, the extensive colour similarity between regions of unhealthy and normal leaves, the presence of noise in the sampling data, and changes in the location, size, and shape of plant leaf, it is difficult to correctly identify and classify plant diseases. In an effort to address these issues, a reliable technique for classifying plant diseases was developed by using a deep AlexNet CNN architecture as the main network with batch normalisation. In the three-step process, the first annotation is made to obtain the RoI (region of interest). The AlexNet CNN is therefore suggested for deep primary feature extraction in a constructed efficient network. The research demonstrates that the existing strategy is superior to more recent ones in terms of accuracy and dependability in recognising diseases in plants. Based on a deep transfer AlexNet CNN model, this research work developed a model for diseases identification and classification in plant leaves. It is trained using additional datasets that include a variety of plant leaf classifications and background images. From Plant Village and Kaggle, we gathered data on healthy and diseased tomato plant leaves. We are obtaining a near-balanced dataset containing ten different leaf disease kinds, such as bacterial, fungal, viral, and nutrient insufficiency. Ten classes have been considered for this research by gathering a dataset with associated images of the typical and abnormal tomato plant leaves. Considered in this work were the various labels for healthy and diseased tomato leaves, such as early blight, Bacterial spot, late bright mold, healthy, etc. Since deep CNN models have shown notable machine vision results, they are used in this case to diagnose and categorise plant illnesses from their leaves. As a result, the proposed CNN models can thus now be evaluated from confusion matrix using data analysis criteria, primarily focusing on metrics for evaluation like training and validation accuracy, loss, Recall, Precision, F1 score, processing speed, and performance.

Identification of Plant Disease Based on Multi-Task Continual Learning

Deep learning excels in the identification of specific plant diseases. However, dealing with multi-domain datasets, which encompass a variety of categories, presents challenges due to limited data availability. (1) Background: In real-world scenarios, data distribution is uneven, the scale continues to grow, new categories emerge, and a phenomenon known as ‘catastrophic forgetting’ occurs. Models rely on a substantial amount of data for labeling and training. (2) Methods: We introduce a two-stage approach. The first stage is the scalable feature learning phase, where the previous feature representation is fixed. Through a new feature extractor, incoming and stored data are trained to expand features. In the second stage, by introducing an auxiliary loss to determine whether key parameters are retained, we reduce the instability of weight parameters. This maintains the separability of old features and encourages the model to learn new concepts, diversity, and discriminative features. (3) Results: Our findings indicate that when the data landscape shifts, recognition accuracy in multi-task continual learning, leveraging the simultaneous availability of datasets, significantly outperforms single convolutional networks and multi-task learning models. (4) Conclusions: Our method advances continual learning towards practical applications. It is particularly effective in mitigating catastrophic forgetting in multi-domain datasets and enhancing the robustness of deep-learning models.

Ensemble of CNN models for classification of groundnut plant leaf disease detection

Plant diseases pose a significant threat to the world's nutrition and can have severe consequences for smallholder farmers who rely on a thriving groundnut crop for their livelihoods. Therefore, it is crucial to create an algorithm for early automated diagnosis of plant diseases. Soa comprehensive dataset of groundnut leaf images was created in collaboration with a pathologist, facilitating the automated identification of plant diseases. The field of image classification has found CNN to be quite effective. In this study, we create such a method for disease identification and classification by utilizing a tri-CNN architecture consisting of DenseNet169, Inception, and Xception —that have been pre-trained on the ImageNet dataset using two created non-linear equations on decision scores from the before mentioned base learners,the outlined ensemble methodology generates ultimate predictions for the test samples by incorporating the scores of four conventional metrics for evaluation, namely recall, precision, accuracy, and f1-score, obtained from the base learners. Hence mentioned CNN customized models are used to train our model for obtaining better results. The proposed approach evaluated on real world groundnut leaf dataset. The model that was put forth attained accuracy rates of 98.46 %.

Classification of Cotton Leaf Diseases using Whales Optimization Algorithm based Deep Neural Network

In the realm of agriculture, a current focal point of research revolves around the identification of plant diseases through leaf imagery. Employing image processing techniques for the recognition of agricultural plant diseases holds promise for reducing farmers' reliance on safeguarding their crops. This paper introduces a novel approach to classify cotton leaf diseases, utilizing a Deep Neural Network enhanced by the Whales Optimization Algorithm (WOA). The dataset comprises 10,000 cotton images sourced from Kaggle.com, directly captured from farm fields, covering healthy leaves, bacterial blight, Anthracnose, Cercospora leaf spot, and Alternaria diseases. Preprocessing involves the application of a median filter to eliminate image noise, and for segmenting diseased and normal regions, the Gustaffson-Kessel (G-K) fuzzy clustering method is employed. The WOA-augmented DNN demonstrates its effectiveness in classifying cotton images.

Agricultural Crop Recommendation, Crop Disease Detection and Price Prediction Using Machine Learning

India's foundation is its agriculture. With over 60% of the workforce employed and producing over 18% of the nation's GDP, it is a vital sector of the Indian economy. Although there are many ways in which we can use technology to increase product production, a farmer can only profit if he is able to sell his crops. Three laws have been passed by the Indian government to encourage the export of agricultural products across the nation. But today, we witness farmers all over the nation fighting against these regulations to protect their rights. Farmers worry that big merchants will exploit them as puppets and undercut the price at which they sell their goods. After doing a thorough analysis of the situation, we developed the concept of creating an agricultural produce application that facilitates direct communication between farmers and retailers, allows for product reviews and crop yielding rate prediction, and predicts the price of agricultural produce based on quantity produced and previous years' sales rates. Unpredictable rains, unexpected temperature decreases, and heat waves have all been brought on by the shifting climate, and the ecosystem has suffered significant harm. Thankfully, machine learning has produced useful methods for tackling international problems, such as agriculture. These climate change-related agricultural issues can be resolved by using various machine learning methods. The purpose of this piece is to Create a method to identify crop diseases and suggest crops. For both objectives, publicly accessible datasets were utilized. Regarding the crop recommendation system, feature extraction was done, and a variety of machine learning methods were used to train the dataset, including Support Vector Machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Multilayer Perceptron. 99.30% accuracy was attained via the random forest algorithm.CNN architectures such as ResNet50, and EfficientNetV2 were trained and compared for the plant disease identification system. EfficientNetV2 outperformed the rest, with a high accuracy of 96.08%.

Plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm.

Most of the food consumed worldwide is produced by plants. Plant disease is a major cause of reduced production, but can be managed with regular monitoring. Manually observing plant diseases takes more time and is error-prone. Early detection of plant diseases with the aid of artificial intelligence and computer vision can decrease the effects of disease and help plants withstand the downsides of continuing surveillance. In this manuscript, plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm (PDI-CMAE-DDAOA) is proposed. The plant village dataset is used to collect the images. Then the image is fed to preprocessing. Using an adaptive self-guided filter approach, the noise is removed from the input images during the pre-processing phase. The result of the pre-processing section serves as input for the feature extraction segment. Four statistical features, including mean, variance, entropy, and kurtosis, are recovered from the cosine similarity hidden Markov model (CSHMM). The contextual mask auto-encoder (CMAE) is given the extracted features to accurately classify the healthy and unhealthy regions of the plant image. The issue of slow convergence affects the CMAE. However, it is noted that the CMAE converges more quickly with deep learning features than with texture features in this instance. The CMAE classifier generally does not exhibit any adaptation of optimization algorithms for determining the best parameters to ensure the precise classification of plant disease. Therefore, dynamic differential annealed optimization algorithm (DDAOA) is considered to enhance the CMAE classifier, which accurately distinguishes between healthy and diseased plants. The proposed PDI-CMAE-DDAOA is done in Python. The efficacy of PDI-CMAE-DDAOA is evaluated under some performance metrics, like accuracy, precision, sensitivity, F1-score, specificity, error rate, receiver operating characteristic curve (ROC), computational time. The proposed method provides higher accuracy 23.34%, 34.33%, and 32.07%; higher sensitivity 36.67%, 36.33%, and 23.21%; higher F1-score 46.67%, 57.56%, and 43.21%; higher specificity 56.67%, 67.56%, and 23.21% analyzed with existing models, like transfer learning-based deep ensemble neural network for plant leaf infection recognition (PDI-DENN), plant disease detection with hybrid model based on convolutional auto-encoder and convolutional neural network (PDI-CAE-CNN), and automatic and reliable leaf disease finding depending on deep learning methods (PDI-EN-CNN), respectively. RESEARCH HIGHLIGHTS: To find the plant disease at early stage. To present PDI-CMAE-DDAOA. To get better classification accuracy by extracting the optimal features with the help of efficient CSHMM. To minimize the error during classification process. To maximize high area under curve value.

Analysis on Leaf Disease Identification using Classification Models

The Researchers have all been aware of the rising food demand brought on by the population's rapid growth and the high mortality rates caused by medical developments. One of the many farming practises where computerization in agriculture has made significant progress is the identification of numerous plant diseases. The focus of almost every nation has shifted towards mechanising agriculture in order to achieve accuracy and precision and to meet the continually increasing demand for food. Identification of plant diseases is one of the most difficult tasks in agriculture and has a significant effect on crop yield. Artificial intelligence has recently begun to concentrate on smart agriculture science.Ground-breaking methods in plant science through deep learning and hyperspectral imaging to locate and recognise plant diseases has been addressed in this study.

OPIA: an open archive of plant images and related phenotypic traits.

High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566225 images with 2417186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.

A Capsule Attention Network for Plant Disease Classification

The identification of plant diseases is one of the most essential and difficult concerns in agriculture, necessitating solutions with a brighter light. With the onset of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms have aided farmers in identifying and classifying plant features with a high degree of intellectual precision. However, accurate disease classification in plants is essential for empowering farmers to cultivate more and produce more. This study therefore presents a unique assembly of attention, capsule, and feedforward classification layers for reaching the maximum classification accuracy for plant diseases. The proposed system uses user-defined customized Convolutional Transfer Learning networks (CTLN) to extract features and the attention networks exclude unnecessary features and highlight only critical features for classification. Finally, the selected characteristics are sent to the Feedforward Capsule networks to improve performance. This paper proposes a paradigm that overcomes the constraints of existing deep learning networks and drastically decreases the computing burden. The suggested network is thoroughly evaluated utilizing Plant Village databases containing over 50,000 photos of healthy and diseased plants. The performance metrics of the proposed method are evaluated and compared to those of other learning networks. Compared to previous models, experimental results indicate that the proposed model has a 99.8 percent accuracy rate, lending support to the new categorization method that benefits farmer well-being.

Deep learning approaches for object recognition in plant diseases: a review

Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. In recent years, the intersection of agriculture and artificial intelligence has become a popular research topic. Researchers have been experimenting with object recognition algorithms, specifically convolutional neural networks, to identify diseases in plant images. The goal is to reduce labor and improve detection efficiency. This article reviews the application of object detection methods for detecting common plant diseases, such as tomato, citrus, maize, and pine trees. It introduces various object detection models, ranging from basic to modern and sophisticated networks, and compares the innovative aspects and drawbacks of commonly used neural network models. Furthermore, the article discusses current challenges in plant disease detection and object detection methods and suggests promising directions for future work in learning-based plant disease detection systems.

Identification of wheat plant disease using hybrid model of pre-processing with CNN

Diagnosis is given top priority in terms of farm resource allocation, because it directly affects the GDP of the country. Crop analysis at an early stage is important for verifying the efficient crop output. Computer vision has a number of intriguing and demanding concerns, including disease detection. After China, India is the world’s second-largest creator of wheat. However, there exist algorithms that can accurately identify the most prevalent illnesses of wheat leaves. To help farmers keep track on a large area of wheat plantation, leaf image and data processing techniques have recently been deployed extensively and in pricey systems. In this study, a hybrid pre-processing practice is used to remove undesired distortions while simultaneously enhancing the images. Fuzzy C-Means (FCM) is used to segment the affected areas from the pre-processed images. The data is then incorporated into a disease classification model using a Convolutional Neural Network (CNN). It was tested using Kaggle data and several metrics to see how efficient the suggested approach was. This study demonstrates that the traditional Long-Short Term Memory (LSTM) technique achieved 91.94% accuracy on the input images, but the hybrid pre-processing model with CNN achieved 95.06 percent accuracy.

Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.

A Binary Neural Network with Dual Attention for Plant Disease Classification

Plant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-performance devices to meet the requirements for classification accuracy. However, agricultural applications have strict cost control and cannot be widely promoted. This paper presents a novel method for plant disease classification using a binary neural network with dual attention (DABNN), which can save computational resources and accelerate by using binary neural networks, and introduces a dual-attention mechanism to improve the accuracy of classification. To evaluate the effectiveness of our proposed approach, we conduct experiments on the PlantVillage dataset, which includes a range of diseases. The F1score and Accuracy of our method reach 99.39% and 99.4%, respectively. Meanwhile, compared to AlexNet and VGG16, the Computationalcomplexity of our method is reduced by 72.3% and 98.7%, respectively. The Paramssize of our algorithm is 5.4% of AlexNet and 2.3% of VGG16. The experimental results show that DABNN can identify various diseases effectively and accurately.