Odisha University of Agriculture and Technology

Rainfall variability and short-term dry spells critically influence agricultural productivity in rainfed regions such as the Western Undulating Zone (WUZ) of Odisha. With over 75% of annual rainfall concentrated in the monsoon months, any deviation in rainfall distribution can lead to moisture stress, crop yield loss, and heightened vulnerability to drought. This study presents a comprehensive trend analysis of dry spells across Kalahandi and Nuapada districts using 30 years (1995–2024) of daily rainfall data. Two indicators were used: consecutive dry days (CDD), and consecutive dry weeks (CDW). Dry spell events of varying durations were extracted for each block, and the Modified Mann–Kendall (MMK) test was applied to detect monotonic trends. Results reveal a strong spatial heterogeneity. In Kalahandi district, several blocks exhibited significant increasing trends in medium and long dry spells. Karlamunda showed the highest concentration of increasing trends across multiple durations (11-, 12-, 13-, 14-, and 15-day spells), followed by Bhawanipatna, Jaipatna and Junagarh. This indicates escalating drought risk and potential intensification of rainfall breaks within the crop-growing season. CDW analysis also confirmed increasing week-scale moisture stress in Karlamunda. Conversely, Nuapada district presented an opposite pattern, with multiple blocks Boden, Nuapada, Komna, and Sinapali, showing decreasing trends in both daily and weekly dry spell frequencies. These reductions suggest a gradual improvement in rainfall stability, although localized vulnerabilities persist, especially in Khariar and certain short-duration dry spells. The findings suggests to prioritize high-risk blocks for water resource allocation, contingency crop planning, and investment in irrigation infrastructure, ensuring agricultural resilience in Western Undulating Zone.

Fault detection in microgrids is a critical element of system stability and uninterrupted power delivery. Herein, a comparative study using LSTM and bidirectional LSTM networks is performed based on three-phase current data for multi-class fault classification. Five major fault types, namely LG, LL, LLG, LLL, and LLLG, were simulated using a Real-Time Digital Simulator (RTDS) under grid-connected and islanded modes. Collected current signals were preprocessed, normalized, and segmented for sequence learning. Later, both models were trained using the best hyperparameter setting to enhance their capabilities and classify faults. To measure how well they identified faults, evaluation metrics, like accuracy, precision, recall, F1-score, and ROC-AUC, were calculated. The results revealed that the Bi-LSTM outperformed the LSTM and classical machine learning models consistently, with more than 99% accuracy for most fault types. More importantly, the proposed framework also checked classification performance for LLLG faults, with the Bi-LSTM model having a test accuracy of 98.8%. These results confirm that the Bi-LSTM model can robustly and precisely classify and detect faults in real time within specific phases of microgrids; therefore, it provides a scalable foundation for the development of intelligent protection in smart power systems.

In this study, a multiscale framework combining the Lifting Wavelet Transform (LWT) and multi-path Convolutional Neural Network (CNN) is proposed to enhance the analysis of histopathological breast cancer images. LWT is employed due to its capability to obtain multi-resolution features that effectively retain key textural details. In particular, a multi-path CNN facilitates the concurrent processing of features at multiple levels of wavelet decompositions, thereby preserving diagnostic information that sole-path CNN models may lose. The approach was evaluated on the BreakHis dataset, which contains 7,638 rated images at varying magnifications, achieving 99.34% test accuracy by combining magnification levels using a Haar wavelet filter. Results obtained from comparative analyses against general CNN models confirm that the new strategy performs better, thereby validating the efficacy of integrating wavelet-based textural extraction and state-of-the-art CNN models for early-stage breast cancer diagnosis.

The study identified novel, drought-responsive markers and potential candidate genes for kernel quality traits under drought stress which are important genomic resources to breed resilient and nutritious groundnut varieties. Nutritional quality traits are crucial in crop breeding, especially with climate change affecting quality and yield. Increasing dry spells in semiarid regions require understanding drought stress effects on oil, protein, and fatty acid contents of groundnut kernels, and the molecular mechanisms governing these traits. The study showed a reduction in oil content and increase in protein content in a MAGIC population of groundnut under drought stress. MAGIC population exhibited high diversity with 13,937 polymorphic markers and are valuable for the genetic dissection of complex traits as compared to a RIL population with 800 polymorphic markers. Genome-wide association study (GWAS) employing multi-locus models such as BLINK, and MLMM identified 45 significant MTAs for oil, protein, oleic acid, palmitic acid, stearic acid, and linoleic acid under normal and drought stress across all chromosomes except A06, B01, B02, B03, B04, B05, and B06. Fourteen SNPs are identified with > 10% PVE linked to six quality traits, seven pleiotropic SNPs associated with multiple traits, and nine SNPs in both models across at least in two environments. Potential candidate genes for quality traits under drought stress are RING finger proteins and protein kinases for oil content; CASP-like protein and geranylgeranyl diphosphate reductase (GLDR) for protein content, and fatty acid desaturase 2, MYB transcription factor, and AMMECR1 family for oleic and palmitic acid content having role in various biosynthetic and lipid pathways. The MTAs identified in this study hold potential for development into assays for genomic-assisted selection to enhance nutritional quality and drought resilience in groundnut.

Floods are emerging as a formidable and recurrent natural hazard, causing multidimensional impacts, including mortality, agricultural damage, economic hardship, infrastructure deterioration, environmental degradation and social disruption. Understanding farmers’ perception of flood consequences is therefore essential for designing effective coping and adaptation strategies. Since perception is a psychological construct, it can be more accurately measured using a standardised scale. Accordingly, this study aimed to develop a reliable and valid scale using the scale product method to quantitatively assess farmers' perception. The scale values of Thurstone and the weights of Likert were combined to produce higher reliability of the scale. From identification of the dimensions to final administration, the scale has undergone various steps. Judges showed substantial agreement on dimension selection, as measured by Kendall's coefficient of concordance (W = 0.762, X2 =182.932, p ≤ 0.01). Initially, 93 statements were compiled from various sources, of which 66 were retained and distributed to 100 judges for evaluation. The judges were requested to rate the relevance of each item on a seven-point continuum ranging from most unfavourable to most favourable. Following Thurstone's elimination criteria, responses from 60 judges were retained for analysis. Based on the S (median) value (2.5 to 6.5) and Q (quartile) values (1.04 to 1.92), 18 statements were eliminated, leaving 48 final items for the scale. The accuracy and reproducibility of the scale were determined through a content validity ratio (CVR) greater than 0.78 and a reliability coefficient of 0.874. Each statement was then assigned a 4-point response continuum to measure farmers' perceptions.