Crop Diseases Identification Using Deep Learning in Application

This comprehensive review paper explores the profound impact of deep learning in the context of agriculture, with a specific focus on its crucial role in crop disease analysis and management. Deep learning techniques have exhibited remarkable potential to revolutionize agricultural practices, enhancing efficiency, sustainability, and resilience. The introductory section sets the stage by emphasizing the significant role of deep learning in agriculture, offering insights into its transformative applications, including disease detection, yield prediction, precision agriculture, and resource optimization. Subsequent sections delve into the fundamental aspects of deep learning, beginning with an exploration of its relevance and its practical implementations in crop disease detection. These discussions illuminate the essential techniques and methodologies that drive this technology, stressing the critical importance of data quality, model generalization, computational resources, and cost considerations. The paper also addresses ethical and environmental concerns, emphasizing the imperative of responsible and sustainable deep learning applications in agriculture. Furthermore, the document outlines the limitations and challenges faced in this field, encompassing data availability, ethical considerations, and computational resource accessibility, offering valuable insights for future research and development. This paper underscores the immense potential of deep learning to revolutionize agriculture by improving disease management, resource allocation, and overall sustainability. While persistent challenges exist, such as data quality and accessibility, the promise of harnessing deep learning to address global food security challenges is exceptionally encouraging. This comprehensive review serves as a foundational resource for ongoing research and innovation within the agricultural domain

Noise Cancellation Using Least Mean Square and Wavelet Transform for Speech Enhancement

   This paper presents about Adaptive Filter Algorithms used in Embedded Signal Processing for Speech Enhancement. Filters are generally used to select or to remove or to separate out particular fixed frequency, but in Adaptive Filters the frequency selection is important as well as the coefficients of Adaptive filter are being updated by the Adaptive Algorithms. Adaptive Filters are the filters whose filter coefficients are updated automatically by the process of steepest descent algorithm. An Adaptive Filter is defined as a self- adjusting system that relies for its operation on a recursive algorithm, which makes it possible for the Filter to perform satisfactorily in an environment where knowledge of the relevant statistics is not available. Least Mean Square (LMS) is the algorithm used to update filter coefficients by subtracting the desired signal from input signal producing error signal which updates the algorithm variables at each iteration repeated iterating process trains itself to the input signal and cancels noise. Wavelet transform is taking the overlapped windowed frames of input signal transforming it from time domain to frequency to understand the spectrogram of signal apply thresholding depending upon the parameters to consider and denoise the signal. Databases of clean speech and Noise speech can be downloaded freely from TIMIT, NOIZEUS, and SpEAR database. Implement the both the filters LMS and Wavelet and compare them to conclude which algorithm works well

Euclid: Early Release Observations -- Globular clusters in the Fornax galaxy cluster, from dwarf galaxies to the intracluster field

International audienceWe present an analysis of Euclid observations of a 0.5 deg$^2$ field in the central region of the Fornax galaxy cluster that were acquired during the performance verification phase. With these data, we investigate the potential of Euclid for identifying GCs at 20 Mpc, and validate the search methods using artificial GCs and known GCs within the field from the literature. Our analysis of artificial GCs injected into the data shows that Euclid's data in $I_{\rm E}$ band is 80% complete at about $I_{\rm E} \sim 26.0$ mag ($M_{V\rm } \sim -5.0$ mag), and resolves GCs as small as $r_{\rm h} = 2.5$ pc. In the $I_{\rm E}$ band, we detect more than 95% of the known GCs from previous spectroscopic surveys and GC candidates of the ACS Fornax Cluster Survey, of which more than 80% are resolved. We identify more than 5000 new GC candidates within the field of view down to $I_{\rm E}$ mag, about 1.5 mag fainter than the typical GC luminosity function turn-over magnitude, and investigate their spatial distribution within the intracluster field. We then focus on the GC candidates around dwarf galaxies and investigate their numbers, stacked luminosity distribution and stacked radial distribution. While the overall GC properties are consistent with those in the literature, an interesting over-representation of relatively bright candidates is found within a small number of relatively GC-rich dwarf galaxies. Our work confirms the capabilities of Euclid data in detecting GCs and separating them from foreground and background contaminants at a distance of 20 Mpc, particularly for low-GC count systems such as dwarf galaxies

Euclid: Early Release Observations -- Globular clusters in the Fornax galaxy cluster, from dwarf galaxies to the intracluster field

International audienceWe present an analysis of Euclid observations of a 0.5 deg$^2$ field in the central region of the Fornax galaxy cluster that were acquired during the performance verification phase. With these data, we investigate the potential of Euclid for identifying GCs at 20 Mpc, and validate the search methods using artificial GCs and known GCs within the field from the literature. Our analysis of artificial GCs injected into the data shows that Euclid's data in $I_{\rm E}$ band is 80% complete at about $I_{\rm E} \sim 26.0$ mag ($M_{V\rm } \sim -5.0$ mag), and resolves GCs as small as $r_{\rm h} = 2.5$ pc. In the $I_{\rm E}$ band, we detect more than 95% of the known GCs from previous spectroscopic surveys and GC candidates of the ACS Fornax Cluster Survey, of which more than 80% are resolved. We identify more than 5000 new GC candidates within the field of view down to $I_{\rm E}$ mag, about 1.5 mag fainter than the typical GC luminosity function turn-over magnitude, and investigate their spatial distribution within the intracluster field. We then focus on the GC candidates around dwarf galaxies and investigate their numbers, stacked luminosity distribution and stacked radial distribution. While the overall GC properties are consistent with those in the literature, an interesting over-representation of relatively bright candidates is found within a small number of relatively GC-rich dwarf galaxies. Our work confirms the capabilities of Euclid data in detecting GCs and separating them from foreground and background contaminants at a distance of 20 Mpc, particularly for low-GC count systems such as dwarf galaxies