Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning

Knowledge of medicinal plant species is necessary to preserve medicinal plants and safeguard biodiversity. The classification and identification of these plants by botanist experts are complex and time-consuming activities. This systematic review’s main objective is to systematically assess the prior research efforts on the applications and usage of deep learning approaches in classifying and recognizing medicinal plant species. Our objective was to pinpoint systematic reviews following the PRISMA guidelines related to the classification and recognition of medicinal plant species through the utilization of deep learning techniques. This review encompassed studies published between January 2018 and December 2022. Initially, we identified 1644 studies through title, keyword, and abstract screening. After applying our eligibility criteria, we selected 31 studies for a thorough and critical review. The main findings of this reviews are (1) the selected studies were carried out in 16 different countries, and India leads in paper contributions with 29%, followed by Indonesia and Sri Lanka. (2) A private dataset has been used in 67.7% of the studies subjected to image augmentation and preprocessing techniques. (3) In 96.7% of the studies, researchers have employed plant leaf organs, with 74% of them utilizing leaf shapes for the classification and recognition of medicinal plant species. (4) Transfer learning with the pre-trained model was used in 83.8% of the studies as a future extraction technique. (5) Convolutional Neural Network (CNN) is used by 64.5% of the paper as a deep learning classifier. (6) The lack of a globally available and public dataset need for medicinal plants indigenous to a specific country and the trustworthiness of the deep learning approach for the classification and recognition of medicinal plants is an observable research gap in this literature review. Therefore, further investigations and collaboration between different stakeholders are required to fulfilling the aforementioned research gaps.

Section: Basic Terminologiesmentioning

confidence: 99%

Section: Basic Terminologiesmentioning

confidence: 99%

Deep learning for medicinal plant species classification and recognition: a systematic review

Mulugeta,

Sharma,

Mesfin

2024

“…These methods suffer from limitations such as subjectivity, prolonged diagnosis time, and dependence on experienced experts ( Dong et al., 2022 ). To address these limitations of traditional methods, plant disease detection based on image analysis and artificial intelligence has emerged as a hot research topic ( Shoaib et al., 2023 ; Xu et al., 2023 ). This emerging approach utilizes images captured from various plant parts such as leaves and stems, followed by computer algorithms for image analysis and recognition, enabling automated detection and diagnosis of plant diseases.…”

Section: Introductionmentioning

confidence: 99%

“…During the plant growth cycle monitoring, unexpected diseases and pests are likely to emerge. Simultaneously, collecting all the existing plant diseases is difficult and even impossible for real-world applications ( Xu et al., 2023 ). Given the dynamic nature of our world, the setup of open-world plant disease detection is more aligned with real-world applications compared to existing closed-set learning and open-set learning settings.…”

Section: Introductionmentioning

confidence: 99%

A New Deep Learning-based Dynamic Paradigm Towards Open-World Plant Disease Detection

Dong,

Fuentes,

Yoon

et al. 2023

Plant disease detection has made significant strides thanks to the emergence of deep learning. However, existing methods have been limited to closed-set and static learning settings, where models are trained using a specific dataset. This confinement restricts the model’s adaptability when encountering samples from unseen disease categories. Additionally, there is a challenge of knowledge degradation for these static learning settings, as the acquisition of new knowledge tends to overwrite the old when learning new categories. To overcome these limitations, this study introduces a novel paradigm for plant disease detection called open-world setting. Our approach can infer disease categories that have never been seen during the model training phase and gradually learn these unseen diseases through dynamic knowledge updates in the next training phase. Specifically, we utilize a well-trained unknown-aware region proposal network to generate pseudo-labels for unknown diseases during training and employ a class-agnostic classifier to enhance the recall rate for unknown diseases. Besides, we employ a sample replay strategy to maintain recognition ability for previously learned classes. Extensive experimental evaluation and ablation studies investigate the efficacy of our method in detecting old and unknown classes. Remarkably, our method demonstrates robust generalization ability even in cross-species disease detection experiments. Overall, this open-world and dynamically updated detection method shows promising potential to become the future paradigm for plant disease detection. We discuss open issues including classification and localization, and propose promising approaches to address them. We encourage further research in the community to tackle the crucial challenges in open-world plant disease detection. The code will be released at https://github.com/JiuqingDong/OWPDD.

“…Acquiring an accurately annotated dataset relies on expert knowledge, which is only sometimes feasible. Deploying current deep learning-based methods in real-world applications may suffer primarily from limited and imperfect data ( Xu et al., 2023 ). In a real scenario, practitioners without computer vision knowledge lack experience in annotating high-quality boxes, and annotators without domain knowledge have difficulties in annotating accurate object boxes.…”

Section: Introductionmentioning

confidence: 99%

An iterative noisy annotation correction model for robust plant disease detection

Dong,

Fuentes,

Yoon

et al. 2023

Self Cite

Previous work on plant disease detection demonstrated that object detectors generally suffer from degraded training data, and annotations with noise may cause the training task to fail. Well-annotated datasets are therefore crucial to build a robust detector. However, a good label set generally requires much expert knowledge and meticulous work, which is expensive and time-consuming. This paper aims to learn robust feature representations with inaccurate bounding boxes, thereby reducing the model requirements for annotation quality. Specifically, we analyze the distribution of noisy annotations in the real world. A teacher-student learning paradigm is proposed to correct inaccurate bounding boxes. The teacher model is used to rectify the degraded bounding boxes, and the student model extracts more robust feature representations from the corrected bounding boxes. Furthermore, the method can be easily generalized to semi-supervised learning paradigms and auto-labeling techniques. Experimental results show that applying our method to the Faster-RCNN detector achieves a 26% performance improvement on the noisy dataset. Besides, our method achieves approximately 75% of the performance of a fully supervised object detector when 1% of the labels are available. Overall, this work provides a robust solution to real-world location noise. It alleviates the challenges posed by noisy data to precision agriculture, optimizes data labeling technology, and encourages practitioners to further investigate plant disease detection and intelligent agriculture at a lower cost. The code will be released at https://github.com/JiuqingDong/TS_OAMIL-for-Plant-disease-detection.