The DDFO optimization is formed by hybridization of dragonfly and mothfly optimizations and the use of the DDFO optimization helps to enhance the performance of the model in plant disease prediction. The feature is efficiently extracted using the Resnet-101 model and the research's superiority ...
The capacity to identify rice leaf disease was limited by the image backgrounds and the conditions under which the images were acquired [41]. DL models for automated identification of rice leaf diseases suffer significantly when evaluated on independent rice leaf disease data. The results of well-k...
generative-adversarial-network resnet transfer-learning vgg16 inceptionv3 plant-disease plant-disease-detection leaf-image-segmentation dnn-models Updated Dec 28, 2019 Python Vignesh227 / Plant-Disease-Prediction Star 22 Code Issues Pull requests Upload leaf image🌱 and predict the plant disease....
Development of weather-based prediction models for leaf rust in wheat in the Indo-Gangetic plains of India (individually) during the critical periods, and a multiple regression with MXT and relative humidity (RH), serve as four disease prediction models, with ... V Kumar,P. - 《European Jour...
The models then analyse each of these images to determine what type of disease the plant has. The disease classification process will be described below. The advantages of the proposed system are its low construction and maintenance costs. At the same time, it is a portable system of low ...
Deep Learning & Transfer Learning: CNNs for Plant Disease Detection, Classification, and Technological InnovationGuest editors: Michael Gomez Selvaraj, Alliance of Bioversity International and International Center for Tropical Agriculture, Colombia Junfeng Gao, University of Aberdeen, United Kingdom View pr...
A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric. 2018;161:272–9. Article Google Scholar Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA. Using deep transfer learning for image-based plant disease identification. Comput Electron ...
[108], to improve the efficiency of the model calculation process to meet the actual agricultural needs, a deep separable convolution structure model for plant leaf disease detection was introduced. Several models were trained and tested. The classification accuracy of Reduced MobileNet was 98.34%, ...
We performed fine-tuning for previously trained CNN models using the DL methodology to evaluate these DL networks. The process of fine-tuning was accomplished by transferring new layers to our plant disease detection and classification problem to replace the deep CNN's last three layers, as describ...
3D-CNN models can be used to extract features jointly across the spatial and spectral dimension for classification of a 3D hyperspectral data. This is particularly useful when information (i.e. the disease signatures) are localized both in spatial and spectral domains thus exhibiting correlations in...