Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases

Barbedo, Jayme Garcia Arnal; Koenigkan, Luciano Vieira; Halfeld-Vieira, Bernardo Almeida; Costa, R. V. da; Nechet, Katia Lima; Godoy, C. V.; Lobo, M.; Patrício, Flávia Rodrigues Alves; Talamini, Viviane; Chitarra, L. G.; Oliveira, S. A. S. de; Ishida, A. K. N.; Fernandes, J. M. C.; Santos, Thiago Teixeira; Cavalcanti, F. R.; Terao, D.; Angelotti, Francislene

doi:10.1109/tla.2018.8444395

Cited by 71 publications

(3 citation statements)

References 18 publications

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“…The dataset used in this research for disease detection in coffee plants contains images of Arabica coffee leaves 34,35 . The dataset was divided into two categories namely: – The leaf dataset and the symptom dataset.…”

Section: Resultsmentioning

confidence: 99%

“…The dataset used in this research for disease detection in coffee plants contains images of Arabica coffee leaves. 34,35 The dataset was divided into two categories namely: -The leaf dataset and the symptom dataset. The former consists of all images of the leaves with proper labeling of each leaf with its corresponding predominant biotic stress and severity.…”

Section: Dataset Descriptionmentioning

confidence: 99%

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A novel deep learning architecture for disease classification in Arabica coffee plants

Ramamurthy

Thekkath

Batra

et al. 2023

Concurrency and Computation

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Several research works on disease detection in coffee plants have been presented in recent years. Leaf miner and rust are the most prevalent diseases in Arabica coffee plants. Early detection of such diseases allows farmer to take diagnostic actions before the infection spreads to neighboring plants. With advancements in drones and artificial intelligence (AI), the automatic detection of leaf diseases is gaining prominence in the field of smart agriculture. Furthermore, it is critical to develop an accurate method for infestation detection with minimal computational complexity. Existing works for plant disease detection utilize pre-trained deep learning models with millions of parameters.A feasible trade-off has to be attained between accuracy and computational complexity for the deployment of such deep networks. This research proposes an effective method for disease detection in Arabica coffee plants using EfficientNetB0 architecture. The architecture of the EfficientNetB0 network was improvised by including a ghost module at its end. This integration allows the network to learn effectively with minimal parameters without compensating for the end accuracy. The proposed model has a total of 4,874,531 parameters which is significantly lesser than most of the state-of-the-art deep learning architectures and achieved an accuracy of 84%.

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Section: Resultsmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

A novel deep learning architecture for disease classification in Arabica coffee plants

Ramamurthy

Thekkath

Batra

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Quadcopter that autonomously traverse and take aerial shots of a specified field for NDVI analysis [112] ---AI-based systems to detect and identify crop disease [47,[74][75][76][77][113][114][115][116][117] Weed mapping and AI-based weed detection [48,71,72] --Pest recognition using AI-based methods [73,118,119] --…”

Section: Weather and Ghgsmentioning

confidence: 99%

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

et al. 2021

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Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sector.

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Image‐based detection and classification of plant diseases using deep learning: State‐of‐the‐art review

Bagga,

Goyal

2024

Urban Agric & Regnl Food Sys

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Plant diseases are assumed to be one of the primary causes regulating food manufacturing and reducing deficits in crop yield, and it is crucial that plant diseases have rapid spotting and identification. Since the demand for food is rising quickly due to population growth, the main issue for any country is plant disease mechanization in agricultural science. Recent developments in deep learning techniques have found use in plant disease recognition, offering a robust tool with exceedingly precise results. Our main goal is to employ deep learning mechanisms to better understand diseases and how to identify them quickly. In this regard, we analyzed 94 publications chosen from the last 7 years (2016–2023) that show how CNN's philosophy has evolved over this period with various approaches to treating plant diseases. Furthermore, a full description of numerous crops, diseases connected to them, various datasets relating to plant diseases, existing CNN models, and customized CNN architectures is provided. The results of this state‐of‐the‐art review can be implemented to comprehend the cutting‐edge trends in the application of deep learning (CNNs) to detect plant diseases as well as pinpoint any research gaps that require the scientific community's attention.

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Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases

Cited by 71 publications

References 18 publications

A novel deep learning architecture for disease classification in Arabica coffee plants

A novel deep learning architecture for disease classification in Arabica coffee plants

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

Image‐based detection and classification of plant diseases using deep learning: State‐of‐the‐art review

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