High-Accuracy Plant Disease Detection: A ResNet18 Transfer Learning Approach for Multi-Class Classification

Power quality disturbances (PQDs) can significantly affect the reliability of electrical power systems, leading to potential equipment damage and operational inefficiencies. Accurate classification of these disturbances is essential for ensuring continuous and reliable service. The study proposes a deep transfer learning (TL) approach for PQD classification. In the proposed work, various single and multiple PQD signals pertaining to 15 different classes have been generated using mathematical models of PQDs adhering to the guidelines of the IEEE 1159 and IEC 61000-4-30 standards. Time-domain PQD signals are first converted into 2D color images using continuous wavelet transform (CWT). These images are then used to re-train modified pre-trained models such as GoogleNet, SqueezeNet, ResNet-18, and ShuffleNet on synthetic PQD data. Various single and combined PQDs are then classified using trained models. Moreover, the performance of the trained models is evaluated with the PQD signals containing noise of various signal-to-noise ratios (SNR), as well as PQD signals collected from the experimental setup in the laboratory. The results signify that the GoogleNet model exhibits consistent performance for classifying PQDs under various conditions, achieving classification accuracy of 99.8% for synthetic noiseless signals, 98.87% for signals with 20 dB SNR, and 98.89% for signals acquired through experimental setup. Furthermore, the trained models were tested using real-world PQD signals, comprising 26 sag and 42 impulse signals. The GoogleNet model achieved the highest classification accuracy, correctly identifying 23 sag and 34 impulse events, thereby demonstrating real-world applicability and robustness of the proposed approach.

In recent times, drastic changes in the climate have brought about a dramatic increase in the prevalence of crop diseases. Such diseases are a huge threat to sustainable agriculture, especially for third-world countries whose economies still rely much on agriculture. Proper disease detection is an essential prerequisite to get timely treatment of crops and prevent reduction in crop yield. In most plant diseases, the leaves may contain some texture variations, which may help in identifying the diseases. However, an identical disease may not appear in a similar fashion for different plant species and visible texture differences may be very negligible. In most cases, plant diseases are classified for all species together, which may miss the species-specific symptoms. In this paper, we propose a two-stage classification scheme to classify plant diseases that divides the whole classification pipeline into two parts: a plant species detection part and a subsequent species-specific disease detection part. In both stages, we employ some efficient deep convolutional neural network (CNN) architectures, namely EfficeintNetB3, NASNetLarge, DenseNet201, MobileNetV2, VGG16 and InceptionV3 with effective transfer learning. Two publicly available datasets, namely Plant Village and IPM, are considered in this paper. Comparative analysis reveals that the proposed two-stage method can perform better than the conventional single-stage methods reported in the literature for plant disease detection.

Recently, ranking and sorting problems have attracted the attention of researchers in the machine learning community. By ranking, we refer to categorizing examples into one of K categories. On the other hand, sorting refers to coming up with the ordering of the data that agrees with some ground truth preference function. As against standard approaches of treating ranking as a multiclass classification problem, in this paper we argue that ranking/sorting problems can be solved by exploiting the inherent structure present in data. We present efficient formulations that enable the use of standard binary classification algorithms to solve these problems, however the structure is still captured in our formulations. We further show that our approach subsumes the various approaches that were developed in the past. We evaluate our algorithm on both synthetic datasets and for a real world image processing problem. The results obtained demonstrate the superiority of our algorithm over multiclass classification and other similar approaches for ranking/sorting data.KeywordsKernel FunctionSynthetic DataDifference VectorOrdinal RegressionRanking ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A review on automated plant disease detection: motivation, limitations, challenges, and recent advancements for future research

Plant diseases have a huge impact on agricultural productivity and global food security. Rapid Identification and timely control of these diseases are crucial in order to reduce yield loss. During recent years deep learning techniques have demonstrated very promising results with regard to automating the detection of plants diseases from leaf images. Transfer learning, one of the popular approaches of deep learning, has been extensively employed to leverage large scale pre trained models and adapt them to new tasks with limited data. This paper presents a study on the application of transfer learning in the detection of plant leaf conditions diseases. A transfer learning approach is proposed where a pre trained convolutional neural. The purpose of this is fine-tuning a CNN model on plant-leaf images for the classification of various diseases.

Convolutional neural networks have demonstrated state-of-the-art performance in image classification and various other computer vision tasks. Plant disease detection is an important area of deep learning which has been addressed by many recent methods. However, there is a dire need to optimize these solutions for resource-constrained portable devices such as smartphones. This is a challenging problem because deep learning models are resource extensive in nature. This paper proposes an efficient method to systematically classify plant disease symptoms using convolutional neural networks. These networks are memory efficient and when coupled with the proposed training configuration it enables rapid development of industrial applications by reducing the training times. Another critical problem arises with the improper distribution of samples among classes known as the class imbalance problem, which is addressed by employing a simple statistical methodology. Transfer learning is a known technique for training small datasets which transfers pre-trained weights learned on a large dataset. However, during transfer learning, negative transfer learning is a common problem. Therefore, a stepwise transfer learning approach is proposed which can help in fast convergence, while reducing overfitting and preventing negative transfer learning during knowledge transfer across domains. The system is trained and evaluated on two plant disease datasets i.e., PlantVillage (a publicly available dataset) and pepper disease dataset provided by the National Institute of Horticultural and Herbal Science, Republic of Korea. The pepper dataset is particularly challenging as it contains images from different parts of the plant such as the leaf, pulp, and stem. The proposed system has dominated the previous works on the PlantVillage dataset and achieved 99% and 99.69% accuracy on the Pepper dataset and PlantVillage datasets, respectively.

Crops play a vital role in the global food supply. Various environmental factors can cause crop diseases and insect pests, resulting in severe production losses. However, manual detection of crop diseases and insect pests is a time-consuming and error-prone process, which often fails to identify and prevent the spread of plant diseases in a timely and effective manner. To overcome these challenges, machine learning (ML) and deep learning (DL) technologies have been introduced in recent years to improve early identification and effective response to plant pests and diseases. In this article, the principles of convolutional neural network (CNN), recurrent neural network (RNN) and adversarial generative neural network (GAN) are explained, as well as the latest progress in the improvement and application of the three algorithms in plant disease detection and detection. By applying DL technology, efficient and accurate detection of pests and diseases can be achieved, thereby providing reliable support for agricultural production. It is hoped that this work will become a valuable resource for researchers studying plant pest and disease detection and provide guidance to further advance research and practice in this field. At the same time, some current challenges and issues that need to be solved are also discussed, such as data quality, target occlusion, model generalization ability, and adaptability in practical applications. Deep learning holds great promise in revolutionizing precision agriculture by providing efficient and accurate solutions for pest and disease management.

RIFATA: Remora improved invasive feedback artificial tree algorithm-enabled hybrid deep learning approach for root disease classification

Abstract: Plant diseases are a major threat to crop yield and food security. Early detection and diagnosis are crucial to prevent the spread of disease and minimize crop losses. Plant diseases can have devastating effects on crop yields and food security. Disease detection is crucial for effective disease management. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNN), have shown promising results in disease detection. In this study, we propose a plant disease detection system using CNN and Arduino. The system involves capturing images of plant leaves using a camera module connected to an Arduino board. In recent years, deep learning techniques such as Convolutional Neural Networks (CNNs) have shown great promise in image-based disease detection. Additionally, the use of low-cost microcontrollers like Arduino can provide a costeffective solution for real-time disease detection in the field. This paper proposes a plant disease detection system that combines CNN and Arduino for early and accurate disease detection. The system utilizes a CNN model to classify plant disease images and an Arduino board to analyze the results and provide real-time feedback to the user. The proposed system is tested on a publicly available dataset of plant disease images, achieving high accuracy, and demonstrating its potential for practical use. The results show that the proposed system can provide an affordable and efficient solution for early plant disease detection, facilitating prompt action and reducing crop losses.

Disease has remained a threat to the existence of every living thing on earth. Particularly in agriculture, disease has remained a major constraint to the success of crop yield and demands urgent solutions, starting with early detection. While many studies have been presented on plant disease detection and control, despite their success, the research gap resides in creating a balance between plant disease detection and farm disease detection. This is because detecting disease in a plant does not necessarily imply that the farm is infected with diseases, and this has resulted in issue of false alarm in the existing system. To address this challenge, YOLOV-5 model was trained with a plant disease dataset considering diverse classes of plants such as corn, waterleaf, tomato, pepper, and cassava. The plant disease model generated was used to develop a farm disease monitoring, detection and notification algorithm, which was converted into mobile application software using Python programming for real-time monitoring notification of farm diseases. This multi-purpose system when tested, reported an average precision mean of 0.95. In addition, experimental validation of the model in maize and watermelon farms reported real-time disease detection and notification which facilitates rapid response and control of the disease by the farmer.

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review

The problem of predicting wrist fractures from X-rays using Artificial Intelligence (AI) methods is addressed. Wrist fractures are the most commonly misdiagnosed fractures because of the complex anatomical structure of the wrist bone which includes several different bones. This research provides a predictive solution to automate the process of wrist fracture classifications and outlines a visualization technique to identify the probable location of the fractured region on the X-rays. This thesis describes a deep learning based approach for wrist fracture classification. Deep convolutional neural network (CNN) based models have been used for wrist fracture classification by combining different optimization techniques. The concept of transfer learning has also been incorporated to investigate if the deep models pre-trained on non-medical images can be used to improve the accuracy for wrist fracture classification task. Advancements in the automation of wrist fracture detection rate using transfer learning approach is reported. After training an ensemble of models, the accuracy of the proposed technique has been validated with the help of senior orthopedic surgeons and radiologists using the developed visualizations that provide probable locations of the fractured region on X-rays. The reported results provide promising solutions that can help reduce the misdiagnosis of wrist fractures, enhance patient care, and help facilitate improvements in patient morbidity.

All people have access to food thanks to agriculture, even in areas with rapid population expansion. In order to ensure that the entire population has access to food, early diagnosis of plant diseases is recommended. However, when crops are still maturing, it might be challenging to predict diseases. The goal of this paper is to inform farmers about the use of machine learning to recognize various pest species in various crop plants. Deep learning establishes a continuing, cutting-edge method for analysing images with significant potential and promising outcomes. DL has expanded into the field of agriculture after demonstrating its effectiveness in a number of applications. Here, we reviewed various research articles that used deep learning methods to address diverse research issues in tomato (Solanum lycopersicum) plants. We look at the study areas in tomato plants where deep learning, data preprocessing, transfer learning, and augmentation approaches are performed. studied dataset details, including the number of photos, classes, and train test to validation ratios that were employed. We also look at comparisons made across different deep learning architectures and analyse the results. The results demonstrated that deep learning techniques beat all other image processing methods, however how well DL works depended on the dataset employed.

Plant diseases will decrease the yield, which will have a negative impact on the economy. Image processing techniques are essential for efficient detection of diseases in plants. Despite the existence of various research works for detection of plant diseases, obtaining enhanced accuracy is still an open challenge. In this work, kernel based Fuzzy C-Means clustering for segmentation along with Discrete Wavelet Transform based feature extraction and deep learning based autoencoder based classification are employed towards enhancing efficient detection of diseases in plant leaves. To test the proposed approach, experimentation has been carried out with different datasets in two different manners, namely, (i) with segmentation and (ii) without segmentation. Results are compared and recommendations are suggested.

BackgroundBanana (Musa spp.) is the most popular marketable fruit crop grown all over the world, and a dominant staple food in many developing countries. Worldwide, banana production is affected by numerous diseases and pests. Novel and rapid methods for the timely detection of pests and diseases will allow to surveil and develop control measures with greater efficiency. As deep convolutional neural networks (DCNN) and transfer learning has been successfully applied in various fields, it has freshly moved in the domain of just-in-time crop disease detection. The aim of this research is to develop an AI-based banana disease and pest detection system using a DCNN to support banana farmers.ResultsLarge datasets of expert pre-screened banana disease and pest symptom/damage images were collected from various hotspots in Africa and Southern India. To build a detection model, we retrained three different convolutional neural network (CNN) architectures using a transfer learning approach. A total of six different models were developed from 18 different classes (disease by plant parts) using images collected from different parts of the banana plant. Our studies revealed ResNet50 and InceptionV2 based models performed better compared to MobileNetV1. These architectures represent the state-of-the-art results of banana diseases and pest detection with an accuracy of more than 90% in most of the models tested. These experimental results were comparable with other state-of-the-art models found in the literature. With a future view to run these detection capabilities on a mobile device, we evaluated the performance of SSD (single shot detector) MobileNetV1. Performance and validation metrics were also computed to measure the accuracy of different models in automated disease detection methods.ConclusionOur results showed that the DCNN was a robust and easily deployable strategy for digital banana disease and pest detection. Using a pre-trained disease recognition model, we were able to perform deep transfer learning (DTL) to produce a network that can make accurate predictions. This significant high success rate makes the model a useful early disease and pest detection tool, and this research could be further extended to develop a fully automated mobile app to help millions of banana farmers in developing countries.