OLID I: an open leaf image dataset for plant stress recognition

Orka, Nabil Anan; Uddin, M. Nazim; Toushique, Fardeen Md.; Hossain, M. Shahadath

doi:10.3389/fpls.2023.1251888

Cited by 3 publications

(2 citation statements)

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“…Most of the currently published datasets have several limitations, such as the small number of samples and image collection in a non-field environment, without addressing the complexity of open fields ( Wang et al., 2021 ). In addition, although sharing saves significant resources and enables benchmarking of image analysis and machine learning algorithms ( Lobet, 2017 ), the datasets publicly available are few ( Orka et al., 2023 ). As a case study, Lu and Young ( Lu and Young, 2020 ) in their survey retrieved 5870 search records, but only 34 datasets complied with the inclusion criteria of public availability (no need for a request to the authors) and image collection in field or quasi-field conditions.…”

Section: Introductionmentioning

confidence: 99%

GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat

Dainelli,

Bruno,

Martinelli

et al. 2024

Front. Plant Sci.

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Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.

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Section: Introductionmentioning

confidence: 99%

GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat

Dainelli,

Bruno,

Martinelli

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Most of the currently published datasets have several limitations, such as the small number of samples and image collection in a non-field environment, without addressing the complexity of open fields (Wang et al, 2021). In addition, although sharing saves significant resources and enables benchmarking of image analysis and machine learning algorithms (Lobet, 2017), the datasets publicly available are few (Orka et al, 2023). As a case study, Lu and Young (Lu and Young, 2020) in their survey retrieved 5870 search records, but only 34 datasets complied with the inclusion criteria of public availability (no need for a request to the authors) and image collection in field or quasi-field conditions.…”

Section: Introductionmentioning

confidence: 99%