Search Results - "CNN"

BrRacemeCounter: Mask R-CNN for Raceme Instance Segmentation by International Center for Tropical Agriculture (CIAT) Tropical Forages Program, Arrechea-Castillo, Darwin Alexis, Cardoso Arango, Juan Andres

“…This is an adapted implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. …”
Get full text

NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals by Chadalavada, K., Anbazhagan, K., Ndour, A., Choudhary, S., Palmer, W., Flynn, J.R., Mallayee, S., Sharada, Pothu, Prasad, Kodukula V.S.V., Varijakshapanicker, Padmakumar, Jones, Christopher S., Kholová, Jana

Subjects: “…convolution neural network (cnn)…”
Get full text

NIRCoreVision: A novel deep learning-based framework with GUI integration for core set selection from NIRS data using 1D CNN and k-means clustering by Singh, Naseeb, Kaur, Simardeep, Ranjan Padhi, Siddhant, John, Racheal, Shruti, Kumar, Amit, Rana, Jai Chand, Bhardwaj, Rakesh, Riar, Amritbir

Get full text

Automated machine learning: A case study of genomic "image-based" prediction in maize hybrids by Galli, Giovanni, Sabadin, Felipe, Yassue, Rafael Massahiro, Galves, Cassia, Carvalho, Humberto Fanelli, Crossa, José, Montesinos-López, Osval Antonio, Fritsche-Neto, Roberto

“…Machine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). …”
Get full text

A mobile-based deep learning model for cassava disease diagnosis by Ramcharan, A., McCloskey, P., Baranowski, K., Mbilinyi, N., Mrisho, L., Ndalahwa, M., Legg, James P., Hughes, D.P.

“…Convolutional neural network (CNN) models have the potential to improve plant disease phenotyping where the standard approach is visual diagnostics requiring specialized training. …”
Get full text

Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy by Houngbo, M.E., Desfontaines, L., Diman, J.L., Arnau, G., Mestres, C., Davrieux, F., Rouan, L., Beurier, G., Marie-Magdeleine, C., Meghar, K., Alamu, E.O., Otegbayo, B., Cornet, D.

“…R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). Conclusion: According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD<3 and R2 <0.8) for predicting amylose content from yam flour, while the CNN proved reliable and efficient method. …”
Get full text

Application of a multi-layer convolutional neural network model to classify major insect pests in stored rice detected by an acoustic device by Balingbing, Carlito B., Kirchner, Sascha, Siebald, Hubertus, Kaufmann, Hans-Hermann, Gummert, Martin, Van Hung, Nguyen, Hensel, Oliver

“…Machine learning technique was applied using CNN with an average accuracy of 84.51% to classify insect pests from the emitted sound profiles. …”
Get full text

A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest by Singh, B. K., Chakraborty, Dulal, Kalra, Naveen, Singh, Jaya

“…On the other hand, the machine learning (CNN) model has better ability to capture lower yields. …”
Get full text

Satellite-based Tracking of Agricultural Adaptation Progress in Sub-Saharan Africa by Reymondin, Louis

“…To facilitate the real-time monitoring of climate adaptation progress, we incorporated a Long Short-Term Memory (LSTM) component alongside a CNN. LSTM is renowned for its ability in time series regression, while CNN offers insights into the spatial structure of the agricultural landscape. …”
Get full text

An improved deep learning procedure for statistical downscaling of climate data by Ahmed M.S. Kheir, Abdelrazek Elnashar, Alaa Mosad, Ajit Govind

“…To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. …”
Get full text

Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability by El Bilali, A., Brouziyne, Youssef, Attar, O., Lamane, H., Hadri, A., Taleb, A.

“…These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. …”
Get full text

Leveraging state of the art computer vision models for tree monitoring in silvopastoral systems by Ruiz-Hurtado, Andres Felipe, Cardoso, Juan Andres

“…Pre-trained models like U-Net are useful for semantic segmentation, while others like Mask R-CNN perform better for instance segmentation, and DeepForest for object detection. …”
Get full text

Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations by Nakajima, Kota, Saito, Kazuki, Tsujimoto, Yasuhiro, Takai, Toshiyuki, Mochizuki, Atsushi, Yamaguchi, Tomoaki, Ibrahim, Ali, Mairoua, Salifou Goube, Andrianary, Bruce Haja, Katsura, Keisuke, Tanaka, Yu

“…This study aims to assess the robustness of a convolutional neural network (CNN) model for rice AGB estimation across five locations in three countries, and to demonstrate the feasibility of robust model via a practical approach. …”
Get full text

Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of Sorghum Crop’s Nitrogen Content by Hammouch, Hajar, Patil, Suchitra, Choudhary, Sunita, El-Yacoubi, A. Mounim, Masner, Jan, Kholová, Jana, Anbazhagan, Krithika, Vanek, Jirí, Qin, Huafeng, Stoces, Michal, Berbia, Hassan, Jagarlapudi, Adinarayana, Chandramouli, Magesh, Mamidi, Srinivas, Prasad, K.V.S.V., Baddam, Rekha

“…We computed five RGB vegetation indices, employed several ML models such as MLR, MLP, and various CNN architectures (season 2021), and compared their prediction accuracy for N-inference on the independent test set (season 2022). …”
Get full text

High-resolution crop-type mapping in northern Ghana by Alabi, Tunrayo, Muthoni, Francis, Uponi, John, Alabi, William, Oluwaleye, Josiah

“…The classification used random forest (RF) and convolutional neural networks (CNN) algorithms. The study area covered approximately 290,000 hectares in the Northern and Upper East region of Ghana. …”
Get full text

High-resolution global map of smallholder and industrial closed-canopy oil palm plantations by Descals, A., Wich, S., Meijaard, E., Gaveau, D.L.A., Peedell, S., Szantoi, Z.

“…The DeepLabv3+ model, a convolutional neural network (CNN) for semantic segmentation, was trained to classify Sentinel-1 and Sentinel-2 images onto an oil palm land cover map. …”
Get full text

Evaluación genética caprinos criollos : Informe n°9 (incluye nacimientos 2020) by Giovannini, Nicolas, Maurino, Maria Julia

“…Este informe tiene como objetivo principal presentar el mérito genético de los caprinos Criollos del Norte Neuquino (CNN) candidatos al próximo servicio del Campo Anexo Pilcaniyeu (CAP) de INTA EEA Bariloche. …”
Get full text

Deep learning-based estimation of rice yield using RGB image by Tanaka, Y, Watanabe, T., Katsura, K., Tsujimoto, Y., Takai, T., Tanaka, T., Kawamura, K., Saito, H., Homma, K., Mairoua, S., Ahouanton, K., Ibrahim, A., Senthilkumar, Kalimuthu, Semwal, V., Corredor, E., El-Namaky, R., Manigbas,N., Quilang, E.J.P., Iwahashi, Y., Nakajima, K., Takeuchi, E., Saito, Kazuki

“…A convolutional neural network (CNN) applied to these data at harvest predicted 70% variation in rice yield with a relative root mean square error (rRMSE) of 0.22. …”
Get full text

Taking stock of Niger’s existing regional and global trade agreements by Traoré, Fousseini

“…The creation of the National Standardisation Council (CNN) as well as the National Board for Ensuring Conformity with Standards (AVCN) are in pursuit of this objective. …”
Get full text

Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images by Fazari, Antonio, Pellicer-Valero, Óscar, Gómez-Sanchís, Juan, Bernardi, Bruno, Cubero, Sergio, Benalia, Souraya, Zimbalatti, Giuseppe, Blasco, José

“…The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. …”
Get full text
Get full text