PlantDoc

Singh, Davinder; Jain, Naman; Jain, Pranjali; Kayal, Pratik; Kumawat, Sudhakar; Batra, Nipun

doi:10.1145/3371158.3371196

Cited by 329 publications

(106 citation statements)

References 9 publications

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“…In this study ( Singh et al, 2020 ), the authors explore the potential of utilizing computer vision techniques for the early and widespread detection of plant diseases. To aid in this effort, a custom dataset, named PlantDoc, was developed for visual plant disease identification.…”

Section: Deep Learning Approaches For Recognizing Imagesmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Deep Learning Approaches For Recognizing Imagesmentioning

confidence: 99%

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

et al. 2023

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“…The AgriNet dataset is a collection of 160142 images belonging to 423 plant classes. The dataset was collected from 19 public datasets ( The TensorFlow Team, Flowers (2019) ; Kumar et al., 2012a ; Nilsback and Zisserman ; Cassava disease classification (Kaggle) ; Olsen et al., 2019 ; Söderkvist, 2016 ; U. C. I. M. Learning, 2016 ; Giselsson et al., 2017 ; Peccia, 2018 ; Chouhan et al., 2019 ; J and Gopal, 2019 ; Krohling et al., 2019 ; Rauf et al., 2019 ; D3v, 2020 ; Huang and Chuang, 2020 ; Huang and Chang, 2020 ; Makerere AI Lab, 2020 ; Marsh, 2020 ; Singh et al., 2020b ) geographically distributed between United States, Denmark, Australia, United Kingdom, Uganda, India, Brazil, Pakistan, and Taiwan. It includes field and lab images from different cameras and mobile devices, and it can perform multiple agricultural classification tasks, such as species, weed, pest, and plant diseases detection.…”

Section: Methodsmentioning

confidence: 99%

The power of transfer learning in agricultural applications: AgriNet

Sahili

Awad

2022

Front. Plant Sci.

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Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we propose AgriNet dataset, a collection of 160k agricultural images from more than 19 geographical locations, several images captioning devices, and more than 423 classes of plant species and diseases. We also introduce AgriNet models, a set of pretrained models on five ImageNet architectures: VGG16, VGG19, Inception-v3, InceptionResNet-v2, and Xception. AgriNet-VGG19 achieved the highest classification accuracy of 94% and the highest F1-score of 92%. Additionally, all proposed models were found to accurately classify the 423 classes of plant species, diseases, pests, and weeds with a minimum accuracy of 87% for the Inception-v3 model. Finally, experiments to evaluate of superiority of AgriNet models compared to ImageNet models were conducted on two external datasets: pest and plant diseases dataset from Bangladesh and a plant diseases dataset from Kashmir.

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“…), cassava ( Manihot esculenta ), and apple ( Malus sp. ) datasets offer close range annotated images of infected plant organs against a clean background, offering a resource for disease diagnosis and severity scoring in collected leaves ( Mohanty, 2016 ; Arsenovic et al, 2019 ; Chouhan et al, 2019 ; Krohling, 2019 ; Parraga-Alava et al, 2019 ; Rauf et al, 2019 ; Tian et al, 2019 ; Nakatumba-Nabende et al, 2020 ; Singh et al, 2020 ). Machine learning models using support vector machines, CNNs, and self-attention CNNs trained on similar datasets were published recently ( Abdu et al, 2020 ; El Abidine et al, 2020 ; Zeng and Li, 2020 ), some of which report increased efficiency when using segmented regions for pathogen identification ( Esgario et al, 2020 ; Karlekar and Seal, 2020 ).…”

Section: Applications Of Htpmentioning

confidence: 99%

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

et al. 2021

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High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

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PlantDoc

Cited by 329 publications

References 9 publications

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

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

The power of transfer learning in agricultural applications: AgriNet

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

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