Ritesh Kumar

Climate change is intensifying the complexity and severity of pest–pathogen interactions in agriculture, threatening global food security. Rising temperatures, elevated CO ₂ levels, shifting precipitation patterns, and extreme weather events are reshaping vector ecology, accelerating insect development, altering pathogen virulence, and disrupting plant defense systems. These changes enhance the frequency of co‐infections, increase herbivore pressures, and promote the emergence and spread of invasive pests and diseases, such as maize lethal necrosis and citrus greening. At the plant level, hormonal crosstalk between salicylic acid (SA) and jasmonic acid (JA) pathways is disrupted, undermining resistance mechanisms, particularly under high temperatures and altered carbon‐to‐nitrogen ratios. Resistance genes and quantitative resistance are increasingly compromised, while existing integrated pest management (IPM) strategies face reduced efficacy due to climate‐sensitive biocontrol agents and pesticides. Effective mitigation demands a paradigm shift toward climate‐resilient crop protection strategies. This includes forecasting models, adaptive surveillance systems, microbiome engineering, and agroecological diversification. Regional vulnerabilities further necessitate context‐specific adaptation and crop breeding approaches. Bridging current knowledge gaps requires long‐term, multifactorial field studies and interdisciplinary research that integrates plant physiology, entomology, pathology, and climatology to develop holistic and future‐ready solutions.

Environmental monitoring plays a crucial role in assessing the health of air and water systems, which are increasingly impacted by anthropogenic activities. Traditional monitoring methods, while effective, are often limited by spatial and temporal constraints, which can hinder real-time decision-making. In this study, we explore the potential of Artificial Intelligence (AI) and machine learning (ML) techniques for enhancing air and water quality assessment. A comprehensive evaluation of various ML models—including Random Forest, Support Vector Machine (SVM), Neural Networks, and K-Nearest Neighbors (KNN)—was conducted to assess their performance in predicting and classifying environmental quality metrics such as PM2.5 (air quality) and pH (water quality). Performance metrics such as accuracy, precision, recall, F1-score, and specificity were used to compare model efficacy.The results indicated that Neural Networks performed robustly across multiple evaluation criteria, while SVM demonstrated high precision and specificity in certain cases. Time-series visualizations of air and water quality data over time were employed, revealing significant spatial and temporal variations in both parameters. Further, graphical analyses using pie charts, histograms, and box-and-whisker plots helped elucidate the distribution and variability of air and water quality levels, providing deeper insights into regional pollution trends. Radar graphs and surface plots illustrated the interplay between environmental factors, demonstrating how quality levels evolve spatially and temporally.Overall, this study showcases the potential of AI-driven approaches for real-time environmental monitoring, offering insights that can guide policy-making and mitigation strategies. The findings suggest that AI models can not only improve the accuracy of environmental assessments but also support more proactive decision-making in the face of environmental challenges. Future research should explore the integration of additional environmental parameters and real-time deployment of these AI-based systems for broader-scale applications.

Aflatoxin being a serious threat to public health and international trade, necessi-tates a comprehensive understanding and calculated response with effective inter-vention strategies. This review methodically looks at several aspects of aflatoxin, starting with how it affects international trade laws through feed and feed ingre-dient regulations. Aflatoxin contamination poses serious challenges to world health and the economy, especially in areas that are already at risk. Examining the varia-bles affecting aflatoxin toxicity, the paper clarifies aflatoxin toxicity's complex me-tabolism and the resulting health effects of exposure. The serious repercussions of aflatoxicosis outbreaks and their connections to illnesses like Aspergillosis and cancer are highlighted by a detailed exploration of prospective decontamination techniques as well as strategies for detecting and capturing aflatoxin. A thorough knowledge of the factors that contribute to aflatoxin contamination demonstrates the intricate interactions between environmental variables, farming methods, stor-age circumstances, and different physical, biological, and nutritional components. Strong detection strategies are required due to aflatoxins’ significant effects on an-imal and human health. The paper describes complete preventative tactics that en-compass manipulation in management strategies. global trade and public health from this pervasive threat.

In the present study, we surveyed five different places for the prevalence of chickpea stunt disease in Gajapati, Odisha, India and found the disease incidence (DI) in the range of 1.3 to 6.24%, respectively. The DI was found in the range of 2.65 to 38.77% on three different dates of chickpea sown crop for six intervals of 15 days each. The insect vector (Aphis craccivora) population on different dated sown crops was found to be in the range of 2.5 to 37, respectively. The acquisition feeding period (AFP) of A. craccivora started at 1 h with a value of 2.77 mean transmission and reached 23.32 mean transmission after 48 h. The inoculation feeding period (IFP) was initiated at 6 h with a value of 3.03 mean transmission and reached 27.22 mean transmission after 48 h. This study indicated A. craccivora as the potential vector influencing the health of chickpea plants.

The decline of honey bee (Apis mellifera L.) populations is of great concern around the world. Among the several key drivers, dissemination of pests and pathogens is potential one. Chalkbrood is very common fungal disease of honey bee, caused due to Ascosphaera apis. In the present study, survey was conducted regarding the prevalence of diseases in A. mellifera beekeeping in Gangetic plains of West Bengal, India. Results confirmed the occurrence of chalkbrood disease in different apiaries, where dead and mummified bee larvae with cotton like chalky white or greyish-black covering were found as physical symptoms of the disease. From three surveyed apiaries, a total of 16 hives out of 113 hives were found to be infected with the diseases; and 46 frames out of 132 frames from the infected 16 hives were found to be affected by the pathogen. Microscopic examination reflected that nearly 87% of the samples collected from the infected frame were found to be positive for the spore of Ascosphaera. The fungus associated with the disease was isolated on Potato Dextrose Agar medium, pure cultured and its genomic DNA was isolated to perform PCR and based on 18s rDNA sequencing by using specific primer pair of ITS-1 and ITS-4, the fungus was identified as Ascosphaera apis. Keywords: 18s rDNA, Bees, Necrotrophs, Phylogeny

In order to feed the growing population, agriculture is a vital component of any country; however, pests pose a constant threat to it. Chemical pesticides are employed to safeguard the crops from the enormous yield loss. These chemical pesticides are boundless in killing crop pests; however, they have detrimental effects on the health of both humans and the environment. Therefore, biological control techniques are being utilised after identifying an environmentally suitable substitute. Due to its well-known biological control mechanism, Trichoderma spp. have been utilised extensively in agricultural applications. The host plants soil and rhizosphere serve as shelter place for Trichoderma. It has the ability to create a variety of secondary metabolites and secretion of important enzymes. Clarification of Trichodermas importance in the prevention and treatment of plant diseases is thus important in order to advance sustainable agriculture. The mechanisms, which include mycoparasitism, antibiosis and competition aid in the management of insect pests and plant pathogens in the soil, seeds, roots, stems, leaves, etc. It is crucial to create new formulations of biocontrol microorganisms with a greater level of stability and survivability in order to implement biocontrol technology in the field and enhance its commercialisation.

Agricultural crops are facing a continuous threat due to biotic and abiotic stresses, thus, limiting the crop productivity, and thereby, threatening food security. Plant roots attract several kinds of microbes that induce resistance in plants against these stresses by enhancing the activity of the antioxidant enzymes, phenolic and other non-phenolic compounds, and thereby, have a beneficial effect on plants. Vast research has been carried out on biocontrol agents to manage soil-borne plant pathogens, but there has been limited success in the development of region-specific, commercially viable microbial inoculants. The present research was framed with a view to screen and evaluate native Pseudomonads from the rhizosphere of different crops in lateritic soils and their exploitation in biotic and abiotic stress management under the red and lateritic zone of West Bengal. In the lateritic area of West Bengal, the lowest pH as well as the highest culturable rhizobacterial population was found in the soil of Bankura. Among all the isolated rhizobacteria, 43.33% were found to be moderately antagonistic against three different soil-borne plant pathogens viz., Rhizoctonia solani, Sclerotinia sclerotiorum and Sclerotium rolfsii—while only 6.67% were found to be very highly antagonistic against these soil-borne plant pathogens. Augmented seeds of tomato, cowpea and French bean with native rhizobacteria enhanced the vigour index and 16.67% of the isolates were found to have a high value of vigour index in the normal and acid stress conditions. Based on acid tolerance, antagonistic activity and the seed vigour assay, a total of 13 isolates from 97 of lateritic area were selected. Out of the 13 selected isolates, 7 were positive for protease and lipase production, 8 isolates were positive for the production of HCN, siderophore and salicylic acid production and 9 isolates were found to be positive for IAA, phosphate solubilisation, amylase and chitinase production. Species of beneficial Pseudomonads such as Pseudomonas aeruginosa, P. fluorescens, P. Plecoglossicida, P. helmanticensis, P. geniculate, P. baetica and P. putida were found. Five isolates were used to study the effect on plant growth in terms of germination (%), root and shoot length, as well as fresh root and shoot weight and disease patterns in terms of pre- and post-emergence damping-off under the semi-field condition.