Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

Audio data compression is used to reduce the transmission bandwidth and storage requirements of audio data. It is the second stage in the audio mastering process with audio equalization being the first stage. Compression algorithms such as BSAC, MP3 and AAC are used as standards in this paper. The challenge faced in audio compression is compressing the signal at low bit rates. The previous algorithms which work well at low bit rates cannot be dominant at higher bit rates and vice-versa. This paper proposes an altered form of vector quantization algorithm which produces a scalable bit stream which has a number of fine layers of audio fidelity. This modified form of the vector quantization algorithm is used to generate a perceptually audio coder which is scalable and uses the quantization and encoding stages which are responsible for the psychoacoustic and arithmetical terminations that are actually detached as practically all the data detached during the prediction phases at the encoder side is supplemented towards the audio signal at decoder stage. Therefore, clearly the quantization phase which is modified to produce a bit stream which is scalable. This modified algorithm works well at both lower and higher bit rates. Subjective evaluations were done by audio professionals using the MUSHRA test and the mean normalized scores at various bit rates was noted and compared with the previous algorithms.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterised by persistent social communication difficulties together with restricted and repetitive behavioural patterns. Early and accurate diagnosis is critical for timely intervention,however, traditional clinical assessment methods require extended time and extensive resources while assessment results depend on evaluator judgement. The development of Artificial Intelligence (AI) technologies including Machine Learning (ML) and Deep Learning (DL) has made it possible to automatically identify ASD through the analysis of EEG data and neuroimaging information and eye-tracking results and behavioural signals. The “black-box” characteristics of these models create two main problems which reduce their effectiveness as predictive tools for medical applications. The introduction of Explainable Artificial Intelligence (XAI) techniques, including SHAP, LIME, and Grad-CAM, provides a framework for improving model interpretability and transparency. Quantum Machine Learning (QML) presents two main benefits through its ability to process high-dimensional data while showing improved performance in computational tasks. This review examines the principal datasets, preprocessing techniques, feature extraction methods, and AI-based detection models used in ASD diagnosis.However, researchers continue to study three main problems which include standardised biomarker deficiencies, data variability and clinical validation shortages. The article presents future research paths which will lead to systems that achieve interpretability and robustness while maintaining clinical usability.