The purpose of this project is to create a deep learning model for crop detection using YOLOv5. Crop detection technology can be valuable for various applications in agriculture, such as monitoring ...

Sugarcane Disease Detection using Deep Learning Sugarcane disease is a significant challenge for the sugar industry in Pakistan, causing crop destruction and financial losses. Early detection and ...

A research team has developed the Point-Line Net, a deep learning method based on the Mask R-CNN framework, to automatically recognize maize field images and determine the number and growth ...

Modern agriculture heavily relies on Site-Specific Farm Management practices, necessitating accurate detection, localization, and quantification of crops and weeds in the field, which can be achieved ...

In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, ...

Keywords: deep learning, detection, classification, segmentation, phenotype, Lidar (light detection and ranging) Citation: Jin S, Su Y, Gao S, Wu F, Hu T, Liu J, Li W, Wang D, Chen S, Jiang Y, Pang S ...

image: Performance of different object detection models: (a) Precision–recall with differentmodels; (b) mAP50 (%) achieved using different models.