Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.

By Ashutosh Kumar Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Arti Singh. Trends in Plant Science, 2018

510 citations recorded by the source corpus.

Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being used by the plant science community to make sense of the large datasets now regularly collected via high-throughput phenotyping and genotyping. We review recent work where DL principles have been utilized for digital image-based plant stress phenotyping. We provide a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Finally, we outline several avenues of research leveraging current and future DL tools in plant science.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/033d9fa654806b22787669979927be5351c9af2f

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Citations in the corpus

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, background): “[201] reviewed a range of deep learning methods and their applications, specifically in plant phenotyping.”
Crop Phenomics and High-throughput Phenotyping: Past Decades, Current Challenges and Future Perspectives. (2020, unknown): “example, combining DL and multiple optical images obtained under different field conditions will create robust models for disease phenotyping and even the early detection of plant stress (Singh et al., 2018).”
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, background): “Thus, the overall performance of a model becomes better than models using hand-engineered features [50].”
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, background): “In order to address the challenges pointed out by Barbedo (2016) new methds need to tackle issues like performance and reliability (Singh et al., 2018).”
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review (2020, background): “Plant stress phenotyping can be categorized into four stages: (1) identification (presence of stress); (2) classification (type of stress); (3) quantification (severity of stress); and (4) prediction (possibility of stress occurrence) [23].”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “There are some research papers previously presented to summarize the research about agriculture (including plant disease recognition) by DL [8], [14], but they lacked some of the recent developments in terms of visualization techniques implemented along with the DL and modiﬁed the famous DL models,…”
Plant Disease Detection and Classification by Deep Learning (2019, methodology): “recognition) by DL [43,54], but they lacked some of the recent developments in terms of visualization techniques implemented along with the DL and modified/cascaded version of famous DL models, which were used for plant disease identification.”