Identification Of Plant Diseases Research Articles

Tomato Fungal Disease Diagnosis Using Few-Shot Learning Based on Deep Feature Extraction and Cosine Similarity

Tomato fungal diseases can cause significant economic losses to farmers. Advanced disease detection methods based on symptom recognition in images face challenges when identifying fungal diseases in tomatoes, especially with limited training images. This study utilized novel techniques designed for limited data scenarios, such as one-shot and few-shot learning, to identify three tomato fungal diseases, i.e., Alternaria solani, Alternaria alternata, and Botrytis cinerea. Automated feature extraction was performed using the ResNet-12 deep model, and a cosine similarity approach was employed during shot learning. The accuracy of diagnosing the three diseases and healthy leaves using the 4-way 1-shot learning method was 91.64, 92.37, 92.93, and 100%. For the 4-way 3-shot learning method, the accuracy improved to 92.75, 95.07, 96.63, and 100%, respectively. These results demonstrate that the proposed method effectively reduces the dependence on experts labeling images, working well with small datasets and enhancing plant disease identification.

A light-weight enhanced multi-level attention network for plant disease identification

A light-weight enhanced multi-level attention network for plant disease identification

Leveraging Edge Computing and Deep Learning for the Real-Time Identification of Bean Plant Pathologies

Beans are essential crops globally, standing out as one of the most consumed and nourishing legumes, thereby playing a significant role in human nutrition and food security. Their cultivation faces several challenges, such as pests, diseases, unpredictable weather patterns, and soil erosion. Of these challenges, diseases are recognized as a key challenge, resulting in a decline in both yield quality and quantity, and inflicting substantial financial losses on farmers.This work proposes a deep learning-based approach for precise in-field identification of diseases in bean plants. We evaluate image classification and object detection models using state-of-the-art Convolutional Neural Network (CNN) architectures to identify Angular Leaf Spot (ALS) and bean rust diseases, key bean diseases in Uganda and the region in general, from smartphone images of bean leaves collected in various districts of Uganda.The dataset employed to train these models is the Makerere University beans image dataset, comprising 15,335 images categorized into three (3) classes (ALS, bean rust, and healthy). To improve in-field performance, the dataset was expanded to include an additional class (unknown class) consisting of a diverse collection of 2,800 images to account for images unrelated to the three (3) predefined classes. Adversarial training was further employed to enhance model robustness in identifying the target classes. In addition, two (2) Out-of-Distribution (ODD) detection techniques, i.e., confidence thresholding and training with an auxiliary class (unknown class), were utilized to handle inputs unrelated to bean leaves.Our results show that our custom CNN, BeanWatchNet, achieved 90% accuracy when tested on unseen data for the classification of the three (3) target classes, i.e., ALS, bean rust and healthy. EfficientNet v2 B0 and BeanWatchNet demonstrated superior performance for the four-class (with unknown class) image classification task, achieving 91% and 90% accuracy, respectively, when evaluated on the test dataset. YOLO v8 exhibited superior performance for the object detection models, attaining mAP@50 of 87.6. The custom CNN model and YOLO v8 model were quantized and deployed across two (2) edge platforms: a smartphone (through a mobile application) and a Raspberry Pi 4B to facilitate in-field disease detection. The benchmarking code and best models are publicly available on GitHub.1

Plant Diseases Prediction Using Machine Learning

The early detection and accurate prediction of plant diseases are crucial for improving crop health and maximizing agricultural productivity. Traditional methods of disease detection, which rely heavily on manual observation, are often timeconsuming, labor-intensive, and prone to human error. Recent advancements in machine learning (ML) have opened new possibilities for developing efficient, automated systems that can predict plant diseases with high accuracy. This paper explores various machine learning techniques, including supervised and deep learning models, for plant disease prediction. By analyzing features such as leaf texture, color, and environmental data, these models can identify patterns indicative of specific diseases. Several datasets, including real-time image data and environmental metrics (such as humidity, temperature, and soil quality), are utilized to train and evaluate the models. The study focuses on key ML algorithms like Support Vector Machines (SVM), Decision Trees, Random Forests, Convolutional Neural Networks (CNN), and Transfer Learning to understand their efficacy in disease prediction. These models are evaluated based on their accuracy, precision, recall, and computational efficiency. The results demonstrate that deep learning models, particularly CNNs, outperform traditional methods in image-based plant disease identification. In addition, environmental data integration improves the predictive power of ML models, enabling proactive disease management strategies

Edge-Cloud Remote Sensing Data-Based Plant Disease Detection Using Deep Neural Networks with Transfer Learning.

The identification and control of plant diseases have become increasingly complex as agricultural lands expand globally. Traditional manual methods of monitoring and diagnosing diseases are not feasible for large-scale farms. The complexity is further heightened by the heterogeneity of data types collected through remote sensing methods, such as images, videos, and sensor readings, which need to be processed in real-time. This paper introduces a novel EdgeCloud Remote Sensing architecture that utilizes Deep Neural Networks (DNNs) with Transfer Learning to enhance the detection of plant diseases using remote sensing data. The proposed system employs a Fuzzy Deep Convolutional Neural Network (FCDCNN) to process multimodal data collected from both edge nodes and satellite point clouds. Transfer learning allows the sharing of model weights between cloud and edge nodes, thus optimizing performance without requiring frequent retraining. Simulations show that the proposed system achieves a 98% detection accuracy and reduces processing time by 25% compared to existing methods. By distributing tasks between edge and cloud resources, the system is able to process large datasets effectively and improve disease detection performance on vast farmlands.

Automatic Classification and Identification of Plant Disease Identification by Using a Convolutional Neural Network

The prompt detection of plant diseases mitigates adverse effects on plants. Convolutional neural networks (CNN) and intense learning are extensively utilized in computer vision and recognition of pattern tasks. Scientists presented several DL algorithms for the detection of plant illnesses. Deep learning (DL) models need many parameters, resulting in extended training durations and complicated implementation on compact devices. This research presents a unique DL model utilizing the inception tier and residual connections. Depthwise differentiated convolution is employed to decrease the variable count. The suggested model has undergone training and evaluation using three distinct plant disease databases. The level of accuracy achieved on the PlantVillage database is 97.2%, on the rice disease database is 98.4%, and on the cassava database is 96.3%. The suggested model attains superior accuracy relative to state-of-the-art DL methods while utilizing fewer variables.

YOLOv5s-Based Image Identification of Stripe Rust and Leaf Rust on Wheat at Different Growth Stages.

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.

Design and Research on Identification of Typical Tea Plant Diseases Using Small Sample Learning

Tea plants are susceptible to diseases during their growth. These diseases seriously affect the yield and quality of tea. The effective prevention and control of diseases requires accurate identification of diseases. With the development of artificial intelligence and computer vision, automatic recognition of plant diseases using image features has become feasible. As the support vector machine (SVM) is suitable for high dimension, high noise, and small sample learning, this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants. An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty (C-DCGAN-GP) was used to expand the segmentation of tea plant spots. Finally, the Visual Geometry Group 16 (VGG16) deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition.

Android-Based App Guava Leaf Diseases Identification using Convolution Neural Network

Guava is an economically significant fruit crop in many regions. Conventional plant disease identification depends on skilled workers visually inspecting samples, which can be laborious and error-prone. Therefore, the goal of this study is to create an Android app that uses InceptionV3 to precisely determine the health of guava plants, with an emphasis on hyperparameter tuning and ideal model selection. Inception V3 was chosen as the base model for this study as it outperformed the 25 pre-trained models from Keras API. The Guava leaves dataset consists of 120 raw images collected from real-time capture and the Google website. The images belong to four categories: Algal Spot, Red Rust, Whitefly, and Healthy leaf. The dataset was split into 60% for training and 40% for validation. To increase the number of images, augmentation was performed to multiply each training by 5 times. The 2048 features were extracted from the second last layer of Inception V3. These features were saved in the Numpy format to facilitate hyperparameter tuning. The best hyperparameter tuning results were achieved with a batch size of 4, a learning rate of 0.001, and the Adamax optimizer for 50 epochs, resulting in a validation accuracy of 0.99. This set of hyperparameters was used to train the model, yielding in a training accuracy of 0.98 and a validation accuracy of 0.9. The trained model was exported in the tflite file format and integrated into an Android app developed using Android Studio. To evaluate the performance evaluation of the app, 15 unseen images per class were used and the app achieved an overall F1-Score of 0.85, a recall of 0.85, a precision of 0.89, and a test accuracy of 0.85.

EQID: Entangled quantum image descriptor an approach for early plant disease detection

In present day agriculture, early and accurate identification of plant diseases is essential for prompt response, which protects crop quality and output. This paper presents the Entangled Quantum-Inspired Deep learning model (EQID), a unique method that improves feature representation and classification in plant disease prediction by utilizing the concepts of quantum computing. Two different datasets with images of potatoes and tomatoes as leaves were used to test the EQID model, which performed better than traditional models. EQID obtained 98.96% accuracy, 98.98% precision, 98.96% recall, and 98.90% F1 score on images of potato leaves. For tomato leaves, comparable outcomes were noted, with accuracy, precision, recall, and F1 score all above 99.61%. The accuracy of disease prediction is greatly increased by the efficient and effective feature representation made possible by the EQID model's inclusion of quantum computing techniques. Additionally, the model outperformed other cutting-edge models such as DenseNet-121, VGGNet 16, and Xception Net, illustrating the potentially revolutionary effects of quantum-inspired models in agriculture. Future work will focus on applying the EQID model to a broader range of crops and plant diseases, as well as incorporating additional data sources to further enhance the model's predictive capabilities.

Identification of Grape Plant Diseases Based on the Leaves using Naïve Bayes

Identification of Grape Plant Diseases Based on the Leaves using Naïve Bayes

Machine Learning-based Prediction of African Swine Fever (ASF) in Pigs

African Swine Fever (ASF) is a contiguous viral disease of the pig with serious economic threats to the pork industry. Early identification of ASF infection is important to support sustainable developments in the ASF industry. There is also a need for a solution to identify the ASF infection as early as possible based on apparent symptoms of ASF to screen the infected animals, that are not targeted in the existing literature. Many machine learning (ML) solutions have been proposed in recent years for the prediction and identification of human, animal, and plant diseases. To deal with ASF in pigs ML-assisted model is proposed for the early identification of ASF infection without medical diagnosis and expert opinion. The data regarding apparent symptoms are collected from Chinese small pig farms. The loss of appetite, weakness, diarrhea, vomiting, coughing, skin redness, and breathing difficulty levels are taken as major apparent symptoms of ASF infection. Moreover, different ML models are also evaluated for their performance in the prediction of ASF infection based on selected apparent symptoms of ASF infection. In this regard, Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree (DT), Random Forests (RF), and Gaussian Naïve Bayes ML models are evaluated for ASF infection prediction. The implementation of the proposed solution reveals that the GNB model is more accurate as compared to the other evaluated models for the identification of ASF infection from the apparent ASF symptoms in infected pig animals, with 94.31\% accuracy. The proposed solution would be very effective in the early screening of ASF-infected pig animals without medical diagnosis and expert judgment.

Analysis of existing techniques for image-based recognition of agricultural crops diseases

An analysis of existing methods for processing and identifying the disease of agricultural crops was carried out. Other methods for identifying informative regions, including Fourier transformation, k-means clustering, Histogram Equalization, Scale-Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) algorithms, as well as their combinations were reviewed. The studied approaches demonstrated high accuracy in the identification and classification of various plant diseases. The study examined hybrid models, such as, for example, logistic regression with decision trees and Extreme Learning Machines (ELM). The accuracy of the Support Vector Machine (SVM), ELM, and Decision Trees algorithms were compared, convinced, that the importance of choosing the right parameters and fine-tuning to improve accuracy is important. The methods of deep learning such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and their usage in the scope of the image recognition, which are also used for disease recognition, were examined. The recognition accuracy of several CNN models was compared: DenseNet121, MobileNetV2, NASNetMobile and EfficientNetB0, and the last demonstrated the best results. The modification of ready-made architectures, including the architecture of the EfficientNetB0 neural network, has been analyzed as a way of adapting existing models to specific recognition requirements.

Plant Disease Identification Based on Encoder–Decoder Model

Plant Disease Identification Based on Encoder–Decoder Model

Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition

Deep learning models have demonstrated great promise in plant disease identification. However, existing approaches often face challenges when dealing with unseen crop-disease pairs, limiting their practicality in real-world settings. This research addresses the gap between known and unknown (unseen) plant disease identification. Our study pioneers the exploration of the zero-shot setting within this domain, offering a new perspective to conceptualizing plant disease identification. Specifically, we introduce the novel Cross Learning Vision Transformer (CL-ViT) model, incorporating self-supervised learning, in contrast to the previous state-of-the-art, FF-ViT, which emphasizes conceptual feature disentanglement with a synthetic feature generation framework. Through comprehensive analyses, we demonstrate that our novel model outperforms state-of-the-art models in both accuracy performance and visualization analysis. This study establishes a new benchmark and marks a significant advancement in the field of plant disease identification, paving the way for more robust and efficient plant disease identification systems. The code is available at https://github.com/abelchai/Cross-Learning-Vision-Transformer-CL-ViT.

Real-Time Plant Disease Identification: Fusion of Vision Transformer and Conditional Convolutional Network With C3GAN-Based Data Augmentation

Real-Time Plant Disease Identification: Fusion of Vision Transformer and Conditional Convolutional Network With C3GAN-Based Data Augmentation

Tomato Leaf Disease Classification by Combining EfficientNetv2 and a Swin Transformer

Recently, convolutional neural networks (CNNs) and self-attention mechanisms have been widely applied in plant disease identification tasks, yielding significant successes. Currently, the majority of research models for tomato leaf disease recognition rely solely on traditional convolutional models or Transformer architectures and fail to capture both local and global features simultaneously. This limitation may result in biases in the model’s focus, consequently impacting the accuracy of disease recognition. Consequently, models capable of extracting local features while attending to global information have emerged as a novel research direction. To address these challenges, we propose an Eff-Swin model that integrates the enhanced features of the EfficientNetV2 and Swin Transformer networks, aiming to harness the local feature extraction capability of CNNs and the global modeling ability of Transformers. Comparative experiments demonstrate that the enhanced model has achieved a further increase in training accuracy, reaching an accuracy rate of 99.70% on the tomato leaf disease dataset, which is 0.49~3.68% higher than that of individual network models and 0.8~1.15% higher than that of existing state-of-the-art combined approaches. The results show that integrating attention mechanisms into convolutional models can significantly enhance the accuracy of tomato leaf disease recognition while also offering the great potential of the Eff-Swin backbone with self-attention in plant disease identification.

Prune-FSL: Pruning-Based Lightweight Few-Shot Learning for Plant Disease Identification

Prune-FSL: Pruning-Based Lightweight Few-Shot Learning for Plant Disease Identification

Residual attention based multi-label learning for apple leaf disease identification

Recent studies suggest that plant disease identification via machine learning approach is vital for preventing the spread of diseases. Identifying multiple diseases simultaneous on a single leaf is one of the most irritating issues in agricultural production. However, the existing approaches are difficult to meet the requirements of production practice in accuracy or interpretability. Here, we present residual attention based multi-label learning framework (RAMDI), a method for predicting apple leaf diseases in natural environment. Built upon an attention based multi-label learning framework, the channel and spatial attention mechanisms are investigated and embedded in residual network for multi-label disease prediction, which takes advantage of channel-wise and spatial-wise attention weights. Experimental results indicate that the RAMDI achieves 0.976 accuracy, 0.986 F-score, and 0.979 mAPs, outperforms the existing state-of-the-art apple leaf disease identification models. RAMDI not only predicts multi-disease on a single leaf simultaneously, but also reveals the interpretability among positive predictions that contribute most to identify the key features that are significant for the leaf diseases. This method achieves the following two achievements. Firstly, it provides a solution for detecting multiple diseases on a single leaf. Secondly, this approach gains an interpretable understanding for apple leaf disease identification.

YOLOv5s-BiPCNeXt, a Lightweight Model for Detecting Disease in Eggplant Leaves.

Ensuring the healthy growth of eggplants requires the precise detection of leaf diseases, which can significantly boost yield and economic income. Improving the efficiency of plant disease identification in natural scenes is currently a crucial issue. This study aims to provide an efficient detection method suitable for disease detection in natural scenes. A lightweight detection model, YOLOv5s-BiPCNeXt, is proposed. This model utilizes the MobileNeXt backbone to reduce network parameters and computational complexity and includes a lightweight C3-BiPC neck module. Additionally, a multi-scale cross-spatial attention mechanism (EMA) is integrated into the neck network, and the nearest neighbor interpolation algorithm is replaced with the content-aware feature recombination operator (CARAFE), enhancing the model's ability to perceive multidimensional information and extract multiscale disease features and improving the spatial resolution of the disease feature map. These improvements enhance the detection accuracy for eggplant leaves, effectively reducing missed and incorrect detections caused by complex backgrounds and improving the detection and localization of small lesions at the early stages of brown spot and powdery mildew diseases. Experimental results show that the YOLOv5s-BiPCNeXt model achieves an average precision (AP) of 94.9% for brown spot disease, 95.0% for powdery mildew, and 99.5% for healthy leaves. Deployed on a Jetson Orin Nano edge detection device, the model attains an average recognition speed of 26 FPS (Frame Per Second), meeting real-time requirements. Compared to other algorithms, YOLOv5s-BiPCNeXt demonstrates superior overall performance, accurately detecting plant diseases under natural conditions and offering valuable technical support for the prevention and treatment of eggplant leaf diseases.