Results 81 to 90 of about 4,054 (209)
IET Image Processing, Volume 19, Issue 1, January/December 2025.We present a hybrid deep learning architecture combining EfficientNetV2, MobileNetV2, and Vision Transformers, augmented with attention mechanisms and multi‐scale feature fusion. Optimised for edge deployment via TensorFlow Lite and integrated with IoT sensors for real‐time soil and field monitoring, the model achieved state‐of‐the‐art performance with
Thomas Kinyanjui Njoroge +2 more
wiley +1 more source
Biotecnología Vegetal, 2016 The search for alternatives to agricultural pesticides used for the management of black Sigatoka (Mycosphaerella fijiensis Morelet) includes the selection of microorganisms strains with potential for the control of this pathogen.
Mileidy Cruz-Martín +5 more
doaj
Improving plantain (Musa spp. AAB) yields on smallholder farms in West and Central Africa [PDF]
, 2016 Plantain is an important staple in West and Central Africa, where it is predominantly grown by smallholder farmers. On-farm data are rare but yields are considered to be low.
Hauser, S., Norgrove, L.
core +1 more source
Image‐based crop disease detection using machine learning
Plant Pathology, Volume 74, Issue 1, Page 18-38, January 2025.Image‐based crop disease detection using machine learning provides a faster, more accurate alternative to traditional methods, with advancements in AI and imaging platforms expanding its effectiveness. Abstract Crop disease detection is important due to its significant impact on agricultural productivity and global food security.
Aria Dolatabadian +5 more
wiley +1 more source
Development of plant epidemiological surveillance networks, data exchanges and joint response strategies in the Caribbean: the french experience [PDF]
, 2009 Plant pests and pathogens have the potential to emerge and spread rapidly, cause severe losses and threaten food security worldwide. Such a threat is increased by the rise of commercial exchanges of germplasm and fresh produce and by global warming. This
Abadie, Catherine +2 more
core
Primeiro relato de Sigatoka-negra no Nordeste paraense. [PDF]
, 2015 A Sigatoka-negra, causada pelo fungo Mycosphaerella fijiensis, é relatada em bananeiras no Nordeste paraense. A doença havia sido encontrada nos municípios paraenses de Almeirim, Porto de Moz, Oriximiná e Medicilândia.
BENCHIMOL, R. L. +5 more
core +1 more source
PLoS Genetics, 2016 The Sigatoka disease complex, caused by the closely-related Dothideomycete fungi Pseudocercospora musae (yellow sigatoka), Pseudocercospora eumusae (eumusae leaf spot), and Pseudocercospora fijiensis (black sigatoka), is currently the most devastating ...
Ti-Cheng Chang +3 more
doaj +1 more source
Predicted economic impact of black Sigatoka on the Australian banana industry
Crop Protection, 2013 While Australia has lifted its outright ban on banana imports, very strict pre-entry requirements remain in place making it prohibitively expensive for foreign suppliers to land product in Australia. These include the establishment and maintenance of areas of low pest prevalence (following guidelines described in FAO, 2005; FAO, 2007, respectively ...
Cook, David +7 more
openaire +2 more sources
Mobile-Based convolutional neural network model for the early identification of banana diseases
Smart Agricultural TechnologyThis study aimed to deploy a deep learning model in a mobile application for the early identification of Fusarium Wilt and Black Sigatoka in bananas.
Christian A. Elinisa, Neema Mduma
doaj +1 more source
Pseudocercospora fijiensis, the cause of black Sigatoka in banana
, 2021 sponsorship: Bill and Melinda Gates Foundation, Grant/Award Number: OPP1093845 (Bill and Melinda Gates Foundation|OPP1093845, Bill and Melinda Gates Foundation|OPP1093845)
Kimunye, Janet +4 more
openaire +1 more source