Abstract Video Analytics is widely used by the internet‐based platforms to govern the mass consumption of videos. Traditionally, it is carried out from the decoded format of the videos. This requires the analytics server to perform both decoding and analytics computation. This process can be made fast and efficient if performed over the compressed format of the videos as it reduces the decoding stress over the analytics server. The field of video analytics from the binarized formats using modern deep learning techniques is still emerging and needs further exploration. This proposed work is based on the same notion. In this work, two analytics tasks that is, classification and object detection are carried out from the binarized videos. The binarized formats are produced by using an already‐designed end‐to‐end video compression network. The experiments have been carried out over standard datasets. The proposed MobileNetv2‐based classification network shows an accuracy of 66% over the YouTube UGC dataset and the YOLOX‐S‐based detection network shows mAP of 45% over IMAGENet datasets. The proposed work shows competitiveness and improvement in the detection outcomes on compressed data and also provides further motivation for the adoption of deep learning‐based video compression in practical analytics domains.

Abstract In agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification.