Saint Mary's College of California

Abstract: This paper proposes an advanced convolutional neural network (ECNN) for the automatic detection and classification of leaf diseases towards precision agriculture. The proposed approach was tested on a standard plant leaf dataset with 54,306 images belonging to 38 classes of leaf diseases, with an 80:20 train/test split. Multi-scale convolutional layers and an attention refinement layer were incorporated to enhance the robustness of the approach in complex field environments. The experimental results show that the proposed ECNN approach resulted in an overall classification accuracy of 98.7%, with precision, recall, and F1-measure values of 98.4%, 98.6%, and 98.5%, respectively. Compared with the latest state-of-the-art models such as EnConv, EfficientNet-B0, and hybrid CNN models, the proposed approach improved the accuracy by 2.1% and reduced the computational complexity by 18.3%. The proposed approach was also found to be reliable in early-stage disease detection and under varying illumination conditions, making it suitable for real-time implementation in smart farming systems. Keywords: Leaf Disease Detection, Enhanced CNN, Precision Agriculture, Attention Mechanism and Deep Learning

BACKGROUND OF THE STUDY: In rural communities, mental illness is often associated with supernatural causes or moral weakness, leading individuals to seek help from faith healers rather than health professionals [18]. In urban settings, although people may recognize mental illness as a medical condition, fear of discrimination and social judgment continues to hinder timely help-seeking [19]. For nurses and other healthcare professionals, understanding prevailing community beliefs is essential. Identifying specific myths and misconceptions can help in designing targeted educational materials that are culturally appropriate and context-specific. OBJECTIVES: The aim of this study is to assess and compare the knowledge regarding myths and misconceptions about mental illness among adults in selected rural and urban communities. MATERIALS & METHODS: Study was conducted in selected rural and urban communities of Lucknow, Uttar Pradesh. The population included adults aged 18 years and above residing in selected rural and urban areas. A total of 60 participants were selected, 30 each from rural and 30 from urban communities. Stratified random sampling was adapted to select the samples. Descriptive statistics was used to analyze the demographic characteristic of participants. Fisher’s Exact Test and odds ratios (OR) were used to compare the frequency of myth-related beliefs about mental illness between rural and urban respondents. RESULTS: Rural adults were more than five times more likely to support the idea that mental disease results from personal weakness (OR = 5.09), even though this difference was not statistically significant (p = 0.080). Adults living in rural areas were more likely to think that mental health issues are uncommon or not significant (OR = 7.25). “People with mental illness are violent and dangerous” was more likely to be endorsed by urban adults (OR = 0.33; p = 0.095).Rural respondents were more likely to support the idea that mental illness is a curse brought on by transgressions from a previous life (OR = 3.06). Respondents from rural areas were more likely to concur that discussing mental health issues brings shame or misfortune (OR = 3.00). The statement “Medication is the only treatment for mental illness” was somewhat more common among respondents from rural areas (OR = 1.40), but the difference was not statistically significant. The only statement that showed a statistically significant rural-urban difference was “Children and adolescents do not experience mental illness” (p = 0.013; OR = 4.69). CONCLUSION: Overall, the study underscores the urgent need for targeted mental health awareness programs, especially in rural communities, focusing on correcting myths, reducing stigma, and promoting a bio psychosocial understanding of mental illness. Community-based education, school mental health programs, and culturally sensitive awareness campaigns can play a key role in improving knowledge and attitudes. Addressing these misconceptions is essential for encouraging early help-seeking, improving treatment acceptance, and ultimately strengthening mental health outcomes across both rural and urban populations.

Traditional centralized power stations face challenges such as high greenhouse gas emissions, reliance on non-renewable resources, and inefficiencies in meeting peak power demand. Additionally, Renewable Energy Sources (RESs) like solar and wind complicate efforts to maintain a consistent power supply and grid stability. This study proposes a Hybrid Renewable Energy System (HRES) that integrates a Photovoltaic (PV) system with a Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS) to address these challenges. The PV system incorporates a High Gain Cubic Boost Converter (HG-CBC), regulated by a Raccoon Optimized Algorithm (ROA) based Proportional Integral (PI) controller, to enhance voltage levels. The DFIG based WECS stabilizes power flow through a Pulse Width Modulation (PWM) rectifier and a PI-controlled PWM generator. Moreover, a 3-phase Voltage Source Inverter (3Φ VSI) converts DC power into AC for grid integration. Experimental verification using MATLAB/Simulink demonstrates a high efficiency of 98%, showcasing the model's ability to significantly boost system reliability and suitability for HRES-powered grids.