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 ...

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

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 ...

A team of researchers has turned the keen eye of AI toward agriculture, using deep learning algorithms to help detect crop disease before it spreads.

Java users can integrate ML into their Spring applications with Spring Boot Starter for Deep Java Library. Apply these frameworks to integrate ML capabilities into microservices for deep learning.

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 ...