Leaf disease image retrieval with object detection and deep metric learning

Enhancing multilevel tea leaf recognition based on improved YOLOv8n

In the tea industry, automated tea picking plays a vital role in improving efficiency and ensuring quality. Tea leaf recognition significantly impacts the precision and success of automated operations. In recent years, deep learning has achieved notable advancements in tea detection, yet research on multilevel composite features remains insufficient. To meet the diverse demands of automated tea picking, this study aims to enhance the recognition of different tea leaf categories. A novel method for generating overlapping-labeled tea category datasets is proposed. Additionally, the Tea-You Only Look Once v8n (T-YOLOv8n) model is introduced for multilevel composite tea leaf detection. By incorporating the Convolutional Block Attention Module (CBAM) and the Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature fusion, the improved T-YOLOv8n model demonstrates superior performance in detecting small and overlapping targets. Moreover, integrating the CIOU and Focal Loss functions further optimizes the accuracy and stability of bounding box predictions. Experimental results highlight that the proposed T-YOLOv8n surpasses YOLOv8, YOLOv5, and YOLOv9 in mAP50, achieving a notable precision increase from 70.5% to 74.4% and recall from 73.3% to 75.4%. Additionally, computational costs are reduced by up to 19.3%, confirming its robustness and suitability for complex tea garden environment. The proposed model demonstrates improved detection accuracy while maintaining computationally efficient operations, facilitating practical deployment in resource-constrained edge computing environments. By integrating advanced feature fusion and data augmentation techniques, the model demonstrates enhanced adaptability to diverse lighting conditions and background variations, improving its robustness in practical scenarios. Moreover, this study contributes to the development of smart agricultural technologies, including intelligent tea leaf classification, automated picking, and real-time tea garden monitoring, providing new opportunities to enhance the efficiency and sustainability of tea production.

Leveraging YOLO deep learning models to enhance plant disease identification

Early automation in identifying plant diseases is crucial for the precise protection of crops. Plant diseases pose substantial risks to agriculture-dependent nations, often leading to notable crop losses and financial challenges, particularly in developing countries. Symptoms such as chlorosis, structural deformities, and wilting, characterize these diseases. However, early identification can be challenging due to symptoms similarity. Researchers using artificial intelligence (AI) for plant disease classification, challenges like data imbalance, symptom variability, real-time performance, and costly annotation hinder accuracy and adoption. This work introduced a novel approach using the You Only Look Once (YOLO) deep learning model, chosen for its exceptional accuracy and speed. The study focuses on analyzing YOLO models, specifically YOLOv3 and YOLOv4, to identify fruit plant diseases. This work examines healthy peach and strawberry leaves, as well as peach leaves affected by bacterial spots and strawberry leaves with scorch disease. These models underwent thorough training using data from the publicly accessible Plant Village dataset. The simulation results were highly promising, numerically YOLOv3 model achieved 97% accuracy and a Mean Average Precision (mAP) of 92%, within a total detection time of 105 s. In comparison, the YOLOv4 model outperformed, with a 98% accuracy and an impressive mean average precision of 98%, all while completing the detection process in just 29 s. YOLOv4 demonstrated lower complexity, significantly faster, and more precise performance, especially in detecting multiple items. Serving as an efficient real-time detector, it holds the potential to transform plant disease diagnosis and mitigation strategies, ultimately leading to increased agricultural productivity and enhanced financial outcomes for developing nations.

General retrieval network model for multi-class plant leaf diseases based on hashing

Traditional disease retrieval and localization for plant leaves typically demand substantial human resources and time. In this study, an intelligent approach utilizing deep hash convolutional neural networks (DHCNN) is presented to address these challenges and enhance retrieval performance. By integrating a collision-resistant hashing technique, this method demonstrates an improved ability to distinguish highly similar disease features, achieving over 98.4% in both precision and true positive rate (TPR) for single-plant disease retrieval on crops like apple, corn and tomato. For multi-plant disease retrieval, the approach further achieves impressive Precision of 99.5%, TPR of 99.6% and F-score of 99.58% on the augmented PlantVillage dataset, confirming its robustness in handling diverse plant diseases. This method ensures precise disease retrieval in demanding conditions, whether for single or multiple plant scenarios.

A novel hierarchical framework for plant leaf disease detection using residual vision transformer

Early detection of plant leaf diseases accurately and promptly is very crucial for safeguarding agricultural crop productivity and ensuring food security. During their life cycle, plant leaves get diseased because of multiple factors like bacteria, fungi, weather conditions, etc. In this work, the authors propose a model that aids in the early detection of leaf diseases using a novel hierarchical residual vision transformer using improved Vision Transformer and ResNet9 models. The proposed model can extract more meaningful and discriminating details by reducing the number of trainable parameters with a smaller number of computations. The proposed method is evaluated on the Local Crop dataset, Plant Village dataset, and Extended Plant Village Dataset with 13, 38, and 51 different leaf disease classes. The proposed model is trained using the best trail parameters of Improved Vision Transformer and classified the features using ResNet 9. Performance evaluation is carried out on a wide aspects over the aforementioned datasets and results revealed that the proposed model outperforms other models such as InceptionV3, MobileNetV2, and ResNet50.

Detection and identification of plant leaf diseases using YOLOv4

Detecting plant leaf diseases accurately and promptly is essential for reducing economic consequences and maximizing crop yield. However, farmers' dependence on conventional manual techniques presents a difficulty in accurately pinpointing particular diseases. This research investigates the utilization of the YOLOv4 algorithm for detecting and identifying plant leaf diseases. This study uses the comprehensive Plant Village Dataset, which includes over fifty thousand photos of healthy and diseased plant leaves from fourteen different species, to develop advanced disease prediction systems in agriculture. Data augmentation techniques including histogram equalization and horizontal flip were used to improve the dataset and strengthen the model's resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, which involved comparing its performance with established target identification methods including Densenet, Alexanet, and neural networks. When YOLOv4 was used on the Plant Village dataset, it achieved an impressive accuracy of 99.99%. The evaluation criteria, including accuracy, precision, recall, and f1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. This study's results demonstrate substantial advancements in plant disease detection and underscore the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant significance for everyone involved in agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection.

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 566 225 images with 2 417 186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18 644 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.

An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

WSVAS: A YOLOv4 -based phenotyping platform for automatically detecting the salt tolerance of wheat based on seed germination vigour

Salt stress is one of the major environmental stress factors that affect and limit wheat production worldwide. Therefore, properly evaluating wheat genotypes during the germination stage could be one of the effective ways to improve yield. Currently, phenotypic identification platforms are widely used in the seed breeding process, which can improve the speed of detection compared with traditional methods. We developed the Wheat Seed Vigour Assessment System (WSVAS), which enables rapid and accurate detection of wheat seed germination using the lightweight convolutional neural network YOLOv4. The WSVAS system can automatically acquire, process and analyse image data of wheat varieties to evaluate the response of wheat seeds to salt stress under controlled environments. The WSVAS image acquisition system was set up to continuously acquire images of seeds of four wheat varieties under three types of salt stress. In this paper, we verified the accuracy of WSVAS by comparing manual scoring. The cumulative germination curves of wheat seeds of four genotypes under three salt stresses were also investigated. In this study, we compared three models, VGG16 + Faster R-CNN, ResNet50 + Faster R-CNN and YOLOv4. We found that YOLOv4 was the best model for wheat seed germination target detection, and the results showed that the model achieved an average detection accuracy (mAP) of 97.59%, a recall rate (Recall) of 97.35% and the detection speed was up to 6.82 FPS. This proved that the model could effectively detect the number of germinating seeds in wheat. In addition, the germination rate and germination index of the two indicators were highly correlated with germination vigour, indicating significant differences in salt tolerance amongst wheat varieties. WSVAS can quantify plant stress caused by salt stress and provides a powerful tool for salt-tolerant wheat breeding.