Stemphylium blight (SB) caused by Ascomycete, Stemphylium botryosum Wallr. has been a serious threat to lentil cultivation, mainly in Bangladesh, Nepal, India, and Canada since its first outbreak in Bangladesh in 1986. The genus Stemphylium Wallr., a dematiaceous hyphomycete, comprises up to 150 species, and is pathogenic on a wide range of plants infecting leguminous as well as nonleguminous crops. In recent years, studies indicated overlapping in morphological characters among the different species under the genus Stemphylium, making the identification and description of species difficult. This necessitates different molecular phylogenetic analysis in species delimitation. Therefore, a detailed understanding of spatial diversity and population structure of the pathogen is pertinent for producing source material for resistance breeding. The role of different weather variables as predisposing factors for the rapid spread of the pathogen necessitates devising a disease predictive model for the judicial application of fungicides. A dearth of information regarding spore biology, epidemiology, race diversity, host-pathogen interaction, and holistic disease management approach necessitates immediate attention towards more intensive research efforts. This is the first comprehensive review on the current state of knowledge and research efforts being made for a better understanding of the SB resistance through cognizing biology, ecology, and epidemiology of S. botryosum and effective disease management strategies to prevent widespread outbreaks of SB. The information regarding the biology and epidemiology of S. botryosum is also crucial for strengthening the “Integrated Disease Management” (IDM) programme. The need for a regional research network is advocated where the disease is becoming endemic.

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.

Seed priming is an effective approach to control diseases in tomato. In this context, the current study was undertaken to confirm the effectiveness of seed priming. Treatments included (1) dry seeds; (2) seed priming with 1 mL of moringa leaf extract diluted with 30 mL of distilled water for 18 hr; (3) seed priming with 1% NaCl for 36 hr; (4) seed priming with 10% polyethylene glycol (PEG) for 12 hr; (5) 100 ppm GA ₃ ; (6) 5% KNO ₃ under dark conditions and (7) 1000 ppm thiourea for 24 hr; (8) distilled water for 12 hr and (9) 2% KH ₂ PO ₄ ; and 0.5 mmol · L ⁻¹ NAA at 4°C for 6 hr. The treated seeds were immediately broadcasted in separate nursery beds; after that 21-day old seedlings were transplanted in main plots. Foliar application of GA ₃ at 100 ppm was sprayed 25 days after the seedlings were transplanted. Two-year results revealed that tomato seeds primed with 2% KH ₂ PO ₄ in combination with GA ₃ , significantly reduced bacterial wilt, leaf spot, early blight, late blight and leaf curl diseases. Additionally, the combination of 10% PEG with GA ₃ has emerged as the most effective for controlling collar rot disease. While PEG decreased the incidence of collar rot, KH ₂ PO ₄ strongly reduced bacterial wilt. Target leaf spot, early blight and late blight were less severe when KH ₂ PO ₄ , KNO ₃ and moringa leaf extracts were used, indicating that seed priming was more crucial for prevention of disease severity. Application of GA ₃ also enhanced the effect of nearly all priming agents.

Rice (Oryza sativa L.) constitutes a primary food source for a significant proportion of the global population and its grain quality directly influences market value and consumer acceptance. Grain discolouration (GD), largely attributable to seed-borne pathogens and occasionally to insect infestation, poses a critical challenge by compromising visual quality and reducing commercial grade. This study evaluated the performance of advanced deep learning models for automated detection and classification of discoloured rice grains, incorporating criteria related to predictive accuracy, computational requirements and environmental sustainability. Among the assessed architectures, DenseNet121 exhibited the highest accuracy (98 %), followed by MobileNet (96 %) and MobileNetV2 (92 %), each demonstrating strong precision, recall and F1 scores. In contrast, VGG19 recorded comparatively lower accuracy (84 %). Computational and energy analysis revealed that DenseNet121, MobileNet and Xception required shorter training durations and lower computational resources, whereas MobileNetV2 and InceptionV3 were comparatively resource-intensive. Notably, MobileNet demonstrated superior energy efficiency, consuming 19.03 watts and generating 0.00952 kg carbon dioxide (CO₂), whereas InceptionV3 consumed 377.27 watts and emitted 0.18864 kg CO₂. To facilitate comprehensive model prioritisation, the technique for order of preference by similarity to ideal solution (TOPSIS) was employed, identifying MobileNet as the optimal model when considering performance, efficiency and environmental impact. The findings highlight the potential applicability of lightweight deep learning architectures, particularly MobileNet and DenseNet121, in real-time rice grading and sorting systems, supporting sustainable post-harvest management, improving grain quality assurance and mitigating seed-borne pathogen dissemination.