Titlebook: Computer Vision and Machine Learning in Agriculture, Volume 3; Jagdish Chand Bansal,Mohammad Shorif Uddin Book 2023 The Editor(s) (if appl

是英寸

書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3影響因子(影響力)




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3影響因子(影響力)學科排名




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3網(wǎng)絡(luò)公開度




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3網(wǎng)絡(luò)公開度學科排名




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3被引頻次




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3被引頻次學科排名




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3年度引用




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3年度引用學科排名




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3讀者反饋




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3讀者反饋學科排名

心胸開闊

Building a Custom Module Manuallyomic losses for farmers and reduced supplies for the sugar industry. In this research, we propose a solution for detecting three classes of sugarcane diseases using the YOLO algorithm. The YOLO version 8 model got a maximum accuracy of 96.67% after being trained and evaluated on a dataset of sugarca

障礙

cravat

Using Module Builder to Build Custom Modulesarliest to plan the food requirement of the rising population. Particularly in the field of computer vision, the deep learning approach has demonstrated superior performance over classical machine learning at identifying complicated structures in high-dimensional data. The proposed work focuses on c

CANE

sterilization

sterilization

Bravado

Extending HTTP Sessions with Terracotta,n tasks, this research focused to identify models complexity, performance metrics and detection accuracy of deep learning-based model to detect crop diseases. Subsequently, this work implicitly depicts detection accuracy corresponds to hardware resources to ascertain trade-offs in relation to domain

CHASE

矛盾心理