[HTML][HTML] Challenges and opportunities in machine-augmented plant stress phenotyping

A Singh, S Jones, B Ganapathysubramanian… - Trends in Plant …, 2021 - cell.com
Plant stress phenotyping is essential to select stress-resistant varieties and develop better
stress-management strategies. Standardization of visual assessments and deployment of …

[HTML][HTML] Wild relatives of maize, rice, cotton, and soybean: treasure troves for tolerance to biotic and abiotic stresses

J Mammadov, R Buyyarapu, SK Guttikonda… - Frontiers in plant …, 2018 - frontiersin.org
Global food demand is expected to nearly double by 2050 due to an increase in the world's
population. The Green Revolution has played a key role in the past century by increasing …

The global burden of pathogens and pests on major food crops

S Savary, L Willocquet, SJ Pethybridge… - Nature ecology & …, 2019 - nature.com
Crop pathogens and pests reduce the yield and quality of agricultural production. They
cause substantial economic losses and reduce food security at household, national and …

Soybean yield loss estimates due to diseases in the United States and Ontario, Canada, from 2015 to 2019

CA Bradley, TW Allen, AJ Sisson… - Plant Health …, 2021 - Am Phytopath Society
Soybean (Glycine max L. Merrill) yield losses as a result of plant diseases were estimated by
university and government plant pathologists in 29 soybean producing states in the United …

An explainable deep machine vision framework for plant stress phenotyping

S Ghosal, D Blystone, AK Singh… - Proceedings of the …, 2018 - National Acad Sciences
Current approaches for accurate identification, classification, and quantification of biotic and
abiotic stresses in crop research and production are predominantly visual and require …

[HTML][HTML] Plant disease identification using explainable 3D deep learning on hyperspectral images

K Nagasubramanian, S Jones, AK Singh, S Sarkar… - Plant methods, 2019 - Springer
Background Hyperspectral imaging is emerging as a promising approach for plant disease
identification. The large and possibly redundant information contained in hyperspectral data …

Soybean yield loss estimates due to diseases in the United States and Ontario, Canada, from 2010 to 2014

TW Allen, CA Bradley, AJ Sisson… - Plant Health …, 2017 - Am Phytopath Society
Annual decreases in soybean (Glycine max L. Merrill) yield caused by diseases were
estimated by surveying university-affiliated plant pathologists in 28 soybean-producing …

SoyNet: Soybean leaf diseases classification

A Karlekar, A Seal - Computers and Electronics in Agriculture, 2020 - Elsevier
According to studies, the human population would cross 9 billion by 2050 and the food
demand would increase by 60%. Therefore, increasing and improving the quality of the crop …

[HTML][HTML] Dissecting the economic impact of soybean diseases in the United States over two decades

AY Bandara, DK Weerasooriya, CA Bradley, TW Allen… - PloS one, 2020 - journals.plos.org
Soybean (Glycine max L. Merrill) is an economically important commodity for United States
agriculture. Nonetheless, the profitability of soybean production has been negatively …

[HTML][HTML] Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems

K Nagasubramanian, S Jones, S Sarkar, AK Singh… - Plant methods, 2018 - Springer
Background Charcoal rot is a fungal disease that thrives in warm dry conditions and affects
the yield of soybeans and other important agronomic crops worldwide. There is a need for …