Results 21 to 30 of about 475,966 (276)

SSM-Net for Plants Disease Identification in Low Data Regime

, 2020
Plant disease detection is an essential factor in increasing agricultural production. Due to the difficulty of disease detection, farmers spray various pesticides on their crops to protect them, causing great harm to crop growth and food standards.
Jadon, Shruti
core   +1 more source

Detection of multi-tomato leaf diseases (late blight, target and bacterial spots) in different stages by using a spectral-based sensor. [PDF]

, 2018
Several diseases have threatened tomato production in Florida, resulting in large losses, especially in fresh markets. In this study, a high-resolution portable spectral sensor was used to investigate the feasibility of detecting multi-diseased tomato ...
de Castro, Ana Isabel, Ehsani, Reza, Lu, Jinzhu, Shi, Yeyin, Wang, Shuang   +4 more
core   +2 more sources

A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand

IEEE Access, 2022
Deep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored.
Muhammad Hammad Saleem, Johan Potgieter, Khalid Mahmood Arif   +2 more
doaj   +1 more source

Finished Genome of the Fungal Wheat Pathogen Mycosphaerella graminicola Reveals Dispensome Structure, Chromosome Plasticity, and Stealth Pathogenesis [PDF]

, 2011
The plant-pathogenic fungus Mycosphaerella graminicola (asexual stage: Septoria tritici) causes septoria tritici blotch, a disease that greatly reduces the yield and quality of wheat.
Goodwin, S.B., M'Barek, S., Ben, Dhillon, B., Wittenberg, A.H.J., Crane, C.F., Hane, J.K., Foster, A.J., Lee, T.A.J., van der, Grimwood, J., Aerts, A., Antoniw, J., Bailey, A., Bluhm, B., Bowler, J., Bristow, J., Burgt, A., van der, Canto-Canché, B., Churchill, A.C.L., Conde-Ferràez, L., Cools, H.J., Coutinho, P.M., Csukai, M., Dehal, P., Wit, P.J.G.M., de, Donzelli, B., Geest, H.C., van de, Ham, R.C.H.J., van, Hammond-Kosack, K.E., Henrissat, B., Kilian, A., Kobayashi, A.K., Koopmann, E., Kourmpetis, Y., Kuzniar, A., Lindquist, E., Lombard, V., Maliepaard, C.A., Martins, N., Mehrabi, A., Nap, J.P.H., Ponomarenko, A., Rudd, J.J., Salamov, A., Schmutz, J., Schouten, H.J., Shapiro, H., Stergiopoulos, I., Torriani, S.F.F., Tu, H., Vries, R.P., de, Waalwijk, C., Ware, S.B., Wiebenga, A., Zwiers, L.H., Oliver, R.P., Grigoriev, I.V., Kema, G.H.J.   +56 more
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Detection of plant leaf diseases using image segmentation and soft computing techniques

Information Processing in Agriculture, 2017
Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural.
Vijai Singh, A.K. Misra
doaj   +1 more source

Single nucleotide polymorphisms from Theobroma cacao expressed sequence tags associated with witches' broom disease in cacao [PDF]

, 2009
In order to increase the efficiency of cacao tree resistance to witches¿ broom disease, which is caused by Moniliophthora perniciosa (Tricholomataceae), we looked for molecular markers that could help in the selection of resistant cacao genotypes.
Carels, Nicolas, Cascardo, Julio Cezar M., Gaiotto, Fernanda Amato, Gesteira, Abelmon, Gramacho, Karina Peres, Lima, Leila S., Lopes, Uilson Vanderlei, Micheli, Fabienne, Novais, R., Pires, José Luis, Zaidan, Humberto A.   +10 more
core   +1 more source

Detection of inflammation- and neoplasia-associated alterations in human large intestine using plant/invertebrate lectins, galectin-1 and neoglycoproteins [PDF]

, 1998
Commonly, plant and invertebrate lectins are accepted glycohistochemical tools for the analysis of normal and altered structures of glycans in histology and pathology.
Bosbach, R., Brinck, U., Gabius, Hans-Joachim, Korabiowska, M.   +3 more
core   +1 more source

Image Processing Based Detection of Fungal Diseases in Plants [PDF]

, 2015
This paper presents a study on the image processing techniques used to identify and classify fungal disease symptoms affected on different agriculture/horticulture crops.
A. Jackura, A. P. Szczepaniak, A. Pilloni, C. Fernández-Ramírez, J. Nys, Joint Physics Analysis Center, M. Albaladejo, M. Mikhasenko, N. Sherrill, T. Skwarnicki, V. Mathieu   +10 more
core   +2 more sources

Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

Agriculture, 2022
Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields ...
Bulent Tugrul, Elhoucine Elfatimi, Recep Eryigit   +2 more
doaj   +1 more source

Development Of An Engineered Bioluminescent Reporter Phage For Detection Of Bacterial Blight Of Crucifers [PDF]

, 2012
Bacterial blight, caused by the phytopathogen Pseudomonas cannabina pv. alisalensis, is an emerging disease afflicting important members of the Brassicaceae family.
Bull, Carolee T., Molineux, Ian J., Rubio, Isael, Schofield, David A., Wechter, W. Patrick, Westwater, Caroline   +5 more
core   +1 more source