Lightweight convolutional neural network model for field wheat ear disease identification

Detection and severity analysis of tea leaf blight based on deep learning

(2021) Gensheng Hu et al.   COMPUTERS & ELECTRICAL ENGINEERING

Disease and pest infection detection in coconut tree through deep learning techniques

(2021) Piyush Singh et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Deep learning for classification and severity estimation of coffee leaf biotic stress

(2020) José G.M. Esgario et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Genetic mapping using a wheat multi-founder population reveals a locus on chromosome 2A controlling resistance to both leaf and glume blotch caused by the necrotrophic fungal pathogen Parastagonospora nodorum

(2020) Min Lin et al.   THEORETICAL AND APPLIED GENETICS

SoyNet: Soybean leaf diseases classification

(2020) Aditya Karlekar et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease

(2020) Utpal Barman et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network

(2019) Qiaokang Liang et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

A low shot learning method for tea leaf’s disease identification

(2019) Gensheng Hu et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Depthwise separable convolution architectures for plant disease classification

(2019) Kamal KC et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Stage-specific functional relationships between Tub1 and Tub2 beta-tubulins in the wheat scab fungus Fusarium graminearum

(2019) Huan Wang et al.   FUNGAL GENETICS AND BIOLOGY

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

(2019) Ramprasaath R. Selvaraju et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

(2019) Artzai Picon et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Squeeze-and-Excitation Networks

(2019) Jie Hu et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

(2018) Artzai Picon et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Deep learning models for plant disease detection and diagnosis

(2018) Konstantinos P. Ferentinos   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Understanding Yield Loss and Pathogen Biology to Improve Disease Management: Septoria Nodorum Blotch - A Case Study in Wheat

(2018) Andrea Ficke et al.   PLANT DISEASE

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

(2018) Xiu Jin et al.   Remote Sensing

A review on the main challenges in automatic plant disease identification based on visible range images

(2016) Jayme Garcia Arnal Barbedo   BIOSYSTEMS ENGINEERING

Multi-resolution mobile vision system for plant leaf disease diagnosis

(2015) Shitala Prasad et al.   Signal Image and Video Processing

Multiple Classifier Combination For Recognition Of Wheat Leaf Diseases

(2013) Yuan Tian et al.   INTELLIGENT AUTOMATION AND SOFT COMPUTING