A reliable and accurate diagnosis and identification system is required to prevent and
manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and …

To address the issues of low accuracy and slow response speed in tea disease
classification and identification, an improved YOLOv7 lightweight model was proposed in …

Introduction Current detection methods for apple leaf diseases still suffer some challenges,
such as the high number of parameters, low detection speed and poor detection …

The agricultural sector contributes to 18.8% of India's Gross Domestic Product (GDP). With
the increase in extreme climatic changes and constant deterioration of the quality of yield in …

Diseases and insect pests of tea leaves cause huge economic losses to the tea industry
every year, so the accurate identification of them is significant. Convolutional neural …

Highlights•YOLO-Tea can simultaneously detect tea shoots and their key points.•YOLO-Tea
is a lightweight end-to-end model.•A dataset of tea shoots including different environments …

The quality and productivity of tea harvests around the world are greatly impacted by tea leaf
diseases. To stop the spread of these diseases and put effective control measures in place …

Introduction Accurate grading identification of tea buds is a prerequisite for automated tea-
picking based on machine vision system. However, current target detection algorithms face …

This study aimed to produce a robust real-time pear fruit counter for mobile applications
using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and …