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Post-harvest loss significantly impacts food security, economy, and the overall health of the entire population. About 30% of fresh produce is lost after harvest every year. Physical, macro-biological, microbiological, and enzymatic factors play key roles in losing quality and quantity of harvested crops. Management practices are strategized and executed to address these factors, bringing about new and improved technologies, along with conventional techniques that are easily available for farmers and handlers and effectively minimize loss. In this review, we have consolidated the various existing processing and value-added methods to reduce the loss of crops after harvest including physical and chemical methods and advanced packaging solutions employed in the food and agricultural sectors. The different physical techniques help to prevent or minimize microbial contamination, moderate enzymatic reactions, and maintain the intended organoleptic properties of the crops. These physical interventions are noteworthy for their environment-friendliness, which aligns with the global trend of agriculture adopting more sustainable and ecologically sensitive methods. Chemical techniques involving the use of preservatives and antimicrobial agents are essential to extend the shelf-life of the produce. Nonetheless, a cautious approach is necessary, considering the possible adverse effects of chemical residues on human health and the environment. Striking an appropriate balance between preservation techniques and sustainable agriculture is crucial for the long-term sustainability of post-harvest management measures. Packaging is another key component in the post-harvest management process. Coatings, intelligent and active packaging, and modified (MAP) monitor and control the interior atmospheric conditions of packed products while serving as strong barriers against external contaminants. These packaging innovations, driven by advancements in materials science and nanotechnology, serve as formidable barriers against spoilage, contributing significantly to reducing food waste and ensuring the availability of fresh produce for extended periods. An integrated and holistic approach to these strategies is essential to ensure food security, decrease losses, extend shelf-life and thus, increase the market availability period and quality of the produce to meet the import and export standards. This will in turn alleviate the burden on the farmers and agricultural sector, thereby improving their economic condition. [ABSTRACT FROM AUTHOR]

With the ever-increasing request for speed and the increasing number of Cyber Attacks are having fast and accurate skill to provide verification that is convenient, rapid and exact. Even though possible that it is very difficult to fool Image Recognition Skill in this makes it helpful in serving preclude fraud. In this paper, we propose a model for pixel wise operations, which is needed for identification of a location point. The computer vision is not limited to pixel wise operations. It can be complex and far more complex than image processing. Initially, we take the unstructured Image Segmentation with the help of K-Means Clustering Algorithm is used. Once complete the preprocessing step then extracts the segmented image from the surveillance cameras to identify the expressions and vehicle images. In the raw image from the surveillance camera is the image of a person and vehicle is to classify with the help DWT. Further, the images of the appearances stood also taken with phenomenon called Smart Selfie Click (SSC). These two features are extracted in-order to identify whether the vehicle should be permitted into the campus or not. Thus, verification is possible. These two images are nothing but reliable object extracted for location identification.

Researchers are interested in Facial Emotion Recognition (FER) because it could be useful in many ways and has promising applications. The main task of FER is to identify and recognize the original facial expressions of users from digital inputs. Feature extraction and emotion recognition make up the majority of the traditional FER. Deep Neural Networks, specifically Convolutional Neural Network (CNN), are popular and highly used in FER due to their inherent image feature extraction process. This work presents a novel method dubbed as EfficientNet-XGBoost that is based on Transfer Learning (TL) technique. EfficientNet-XGBoost is basically a cascading of the EfficientNet and the XGBoost techniques along with certain enhancements by experimentation that reflects the novelty of the work. To ensure faster learning of the network and to overcome the vanishing gradient problem, our model incorporates fully connected layers of global average pooling, dropout and dense. EfficientNet is fine-tuned by replacing the upper dense layer(s) and cascading the XGBoost classifier making it suitable for FER. Feature map visualization is carried out that reveals the reduction in the size of feature vectors. The proposed method is well-validated on benchmark datasets such as CK+, KDEF, JAFFE, and FER2013. To overcome the issue of data imbalance, in some of the datasets namely CK+ and FER2013, we augmented data artificially through geometric transformation techniques. The proposed method is implemented individually on these datasets and corresponding results are recorded for performance analysis. The performance is computed with the help of several metrics like precision, recall and F1 measure. Comparative analysis with competent schemes are carried out on the same sample data sets separately. Irrespective of the nature of the datasets, the proposed scheme outperforms the rest with overall rates of accuracy being 100%, 98% and 98% for the first three datasets respectively. However, for the FER2013 datasets, efficiency is less promisingly observed in support of the proposed work.