Titlebook: Computer Vision and Machine Learning in Agriculture, Volume 2; Mohammad Shorif Uddin,Jagdish Chand Bansal Book 2022 The Editor(s) (if appl

breadth

locus-ceruleus

同謀

Real-Life Agricultural Data Retrieval for Large-Scale Annotation Flow Optimization,More advanced architectures such as transformers have also not been applied to this data before. This chapter presents a solution to speed up annotation time by providing annotators semantically similar images to their target image. An image retrieval task is conducted to map crop images to a single

Basal-Ganglia

fodlder

Agri-Food Products Quality Assessment Methods,essment. It can provide qualitative and quantitative data under single analysis. This chapter ensures a critical review on spectroscopic and imaging techniques combined chemo metric analysis, which achieves better accuracy of 99% for food quality analysis, role of machine learning and deep learning

indignant

,ESMO-based Plant Leaf Disease Identification: A?Machine Learning Approach,detects and classifies input plant leaf data as healthy or diseased using SVM and kNN classifier, where SVM gives better accuracy of 93.67%. The obtained results indicate that the proposed methodology outperforms the other algorithms in obtaining good classification accuracy.

大喘氣

Apple Leaves Diseases Detection Using Deep Convolutional Neural Networks and Transfer Learning,isease classes. The dataset is improved and expanded using various data augmentation techniques on the training images. Experimental analysis on the Plant Pathology 2021-FGVC8 dataset shows that our proposed model achieves remarkable precision, recall, and .1-score of 0.9743, 0.9541, and 0.9625, res

thwart

小歌劇

Early Stage Prediction of Plant Leaf Diseases Using Deep Learning Models, CNN-SVM classifier is shown to be a fast, extremely efficient method for classifying specific imaging features into desired disease classes, as well as giving preferable results over the plain CNN and other classifiers, such as the support vector machine (SVM) for large datasets. Finally, the exper

rectocele

2524-7565 . The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies..978-981-16-9993-1978-981-16-9991-7Series ISSN 2524-7565 Series E-ISSN 2524-7573