Citrus greening disease, also known as Huanglongbing (HLB), caused by the bacterium Candidatus Liberibacter asiaticus (CLas), has a detrimental effect on plants and can be a factor in citrus decline, a major threat worldwide to the citrus industry. The reactions of different Citrus species to post-HLB infection are still enigmatic. Therefore, nine prominent Citrus species (Citrus reticulata, C. sinensis, C. limonia, C. karna, C. trifoliata, C. jambhiri, C. volkameriana, C. maxima, and C. latipes) were studied in the field experiment to understand their physiological, biochemical, nutritional, and enzymatic responses to HLB infection. Based on the morphological appearance of the plants, the incidence of CLas was confirmed using gene-based DNA markers OI1/OI2c (1160 bp) and A2/J5 (703 bp). The result showed that HLB incidence ranged from 0 to 100% across different Citrus species (PCR-based). Interestingly, C. latipes showed no typical symptoms and tested negative by PCR. Contrastingly, the incidence in other species was 91.7% in C. maxima, 80.0% in C. trifoliata, and 100% in the remaining cCitrus species. The severity of the symptoms ranged from 61.08 ± 7.5% (C. sinensis) to 0.69 ± 0.2% (C. latipes). In the infected species, C. trifoliata and C. maxima recorded the least reduction in chlorophyll (Chl), net photosynthetic rate (Pn), stomatal conductance (gs), nutrients, and enzyme activities. Comparative analysis revealed that the HLB-infected species exhibited lower Chl, Pn, gs, nutrient levels, and antioxidant enzyme activities. In contrast, potassium, protein, stress biomarkers (proline, H2O2, MDA), and starch content were higher in the HLB-infected plants. Therefore, C. latipes and C. trifoliata are immune to HLB and can be utilised in breeding and as rootstocks for commercial citrus cultivars.

The north-eastern hilly region of India, being part of the Himalaya and Indo-Burma biodiversity hotspots, is endowed with enormous rice diversity, as the cultivation is practiced across varied ecosystems—ranging from mountain peaks and slopes to fertile valley lands. Despite the rich biodiversity, the region is facing declined productivity and gradual genetic erosion over time. Harnessing the untapped potential of rice genetic diversity is crucial for enhancing the productivity, resilience, and sustainability of rice farming in these fragile ecosystems. High heritability (H2) coupled with high genetic advance over mean (GAM) for grain yield and its component traits indicated the predominance of additive gene action and greater scope for genetic improvement through selection. Model-based path coefficient analysis revealed that panicle length, harvest index, and test weight exerted strong positive direct effects on grain yield. Morphological clustering using Euclidean distance grouped the genotypes into six clusters, with sub-cluster III comprising high-yielding genotypes. Using 48 polymorphic microsatellite loci, 179 alleles were amplified among 199 genotypes with an average of 3.66 alleles per locus. The polymorphic information content (PIC) of the marker ranged from 0.005 to 0.776 with a mean of 0.485. Based on analysis of molecular variance (AMOVA), 10.90

Lablab purpureus L., though nutritionally rich, remains underutilized due to limited characterization and inclusion in mainstream breeding programs. This study addressed this gap by evaluating nutritional and yield diversity across 110 Lablab bean accessions collected from ten agro-ecologically diverse Indian states. The analysis aimed to identify nutrient-dense and high-yielding genotypes through multivariate data assessment. Significant genotypic variability was recorded across key biochemical traits including starch (22.4-35.9 g/100 g), amylose (13.1-16.1 g/100 g), protein (21.0-27.1 g/100 g), total soluble sugars, fat, phenolics, fatty acids, and minerals (calcium, phosphorus, iron, zinc, and copper), while seed yield ranged from 19.4 to 335 g/plant. Hierarchical Cluster Analysis grouped accessions into three distinct clusters (p < 0.001): Cluster I represented high-starch and high-yield types, Cluster II comprised protein-and mineral-rich genotypes, and Cluster III included fat-and oleic acid-rich accessions. Principal Component Analysis explained over 53% of total variation, revealing a protein-starch trade-off and positive mineral associations. Correlation analysis identified 35 significant interactions supporting biochemical interdependence among traits. This study presents the first comprehensive multivariate characterization of diverse Indian lablab bean germplasm, establishing an integrated genotype-trait framework to identify distinct clusters and prioritize elite genotypes with superior nutritional and agronomic profiles. The findings provide actionable targets for trait-specific breeding and biofortification, and support the development of functional food formulations and future multi-location validation to accelerate lablab bean mainstreaming in sustainable, nutrition-secure agriculture.

IntroductionThe multiple nutritional disorders producing the early decline of citrus productivity are commonly observed across the citrus belts of northeast India. This situation is further compounded by a mismatch between annual addition and consumption of fertilizers, in the backdrop of an erroneous diagnosis of nutrient imbalance. In this background, we attempted to diagnose nutrient balance in Khasi mandarin (Citrus reticulata Blanco) orchards using several diagnostic tools comprising machine learning (ML) tools.MethodsA database of soil available nutrients (KMn04-N, Brays-P, NH40Ac-K, DTPA-Fe, DTPA-Mn, DTPA-Cu, DTPA-Zn) and fruit yield documenting 180 Khasi mandarin orchards of seedling origin (10–30 years old with row-to-row distance of 4 m and trees 6 m apart) raised under rainfed conditions in the Meghalaya state of northeast India. Diagnosis methods were compared: the sufficiency level of available nutrients (SLAN), the basic cation saturation ratio (BCSR), the compositional nutrient diagnosis (CND), the diagnosis and recommendation integrated system (DRIS) and ML tools like random forest and xgboost.ResultsSoil test interpretation of a low-yielding and nutritionally imbalanced orchard differed among diagnostic methods. DRIS predicted deficient-to-low concentrations of Zn, Ca, P, N, and K; other nutrients like Fe, Cu, Mn, and Mg were at optimum-to-high concentrations. CND standards diagnosed Zn deficiency and Cu excess with potential agronomic manifestations for early decline in productivity. SLAN interpretation was highly skewed for Ca; moderately skewed for Mg, Cu and Zn, and unskewed for N, P, K, Fe and Mn. The accuracy of ML regression models relating nutrient expressions to fruit yield was invariably high, followed by SLAN. The ML xgboost regression model exhibited the highest accuracy in predicting fruit yield from soil test. Conversely, the BCSR, which considers only three cationic dual ratios, was inaccurate. There were distortions when relating concentration values to DRIS indices to determine ‘optimum’ concentration ranges. The ML classification models showed that concentration values were also less accurate than clr to classify data as true negative (TN) or true positive (TP). The xgboost classification model showed optimum ranges for N, P, Ca, Mg, Mn, and Cu; whereas K, Fe, and Zn fell below the lower limits.ConclusionML, hence, as a classification approach, aided in discarding cases of poor yields and high yields showing luxury nutrient consumption or suboptimal nutrient levels. The soil test standards and site-by-site comparisons can further support site-specific nutrient management and precision fertilization. Considering Zn as the most deficient nutrient, Zn biofortification interventions are recommended to increase fruit yield and quality in Khasi mandarin.

Rapid, eco-friendly, and non-destructive estimation of protein content is crucial for efficient nutritional phenotyping and large-scale germplasm screening in legumes. Traditional biochemical methods are time-consuming, costly, and labor-intensive, posing challenges to breeders and the food industry. This study aimed to develop and validate universal near-infrared spectroscopy (NIRS)-based predictive models for protein quantification across multiple legume species. A genetically diverse dataset comprising 1,169 grain samples from cowpea, mung bean, horse gram, pea, lentil, faba bean, winged bean, adzuki bean, rice bean, lablab bean, and chickpea was utilized. Spectral data (1100–2498 nm) were preprocessed using Standard Normal Variate, detrending, derivatives, and smoothing techniques. Two models; Modified Partial Least Squares (MPLS) and one-dimensional Convolutional Neural Network (1D CNN) were developed and validated on an independent set of 351 samples. The 1D CNN model outperformed MPLS, achieving R² = 0.883 and RPD = 2.932, compared to MPLS (R² = 0.814; RPD = 2.320), demonstrating greater accuracy and robustness. This is the first report of a universal NIRS-based deep learning model for protein prediction across diverse legumes. Its integration into portable NIR sensors can accelerate field-based protein screening, enhancing breeding efficiency, gene bank evaluations, food quality control, and the development of functional foods.

Buckwheat, an underutilized crop, is a multipurpose crop with great potential and high nutritive values. Therefore, this study aimed to evaluate the genetic variability of 102 génotypes of common (Fagopyrum esculentum) and tartaty buckwheat (Fagopyrum tataricum) based on agro-morphological traits and microsatellite markers with the identification of high-yielding and stable genotypes with superior nutritive values. The accessions varied significantly in terms of morpho-molecular and biochemical traits. Key traits with agronomic relevance namely, number of seeds per plant, hundred seed weight, and petiole length were identified to exhibit positive correlations with direct positive path coefficient on yield per plant. Both the agro-morphological and SSR based clustering grouped the genotypes into five major clusters. The SSR polymorphism analysis, gene diversity and heterozygosity revealed substantial genetic diversity among the populations. The first three principal components explained about 61.05% of the total variance indicating trait under study contribute maximum of the variations. The model-based structure and PCoA analysis differentiated the population into two sub-populations. The genetic variation, as shown by analysis of molecular variance (AMOVA) indicates higher variability within population than among population in the analyzed set. Further, the AMMI stability analysis identified five promising accessions. Based on the multi-trait stability index, combining all the morphological traits together, IC37275, IC26592, IC13139, IC36919, and IC26606 were identified with a selection efficiency of 20%. Overall, the study revealed deep insights into genetic diversity, population differentiation vis-à-vis the identification of high yielding and stable accessions with higher nutritive values. Taking into account the nutritional values with yield and stability, the following four accessions namely IC16550, IC26586, Shimla B-1, and Local teethey were identified as most promising and thus exhibit a great potential to be utilized in buckwheat improvement programs.

Identification of rice varieties with tolerance to Al3+ toxicity and P deficiency and their judicious utilization in breeding program could be one of the most economical and environment-friendly approaches towards increasing crop productivity in north-eastern regions predominated by acidic soil conditions. A set of 92 rice genotypes, including five released varieties as checks, was evaluated for yield and component traits in field conditions, as well as root and biomass traits in modified ammonium nitrate-free formulations of Yoshida and Magnavaca solutions for low P and high Al conditions, respectively. The Marker-trait association was also validated using north-eastern genotypes with a set of 13 gene-based Markers associated with high Al and low P. The genotypes under Al toxicity conditions had expressed stunted root length and an increased number of secondary roots. In the case of P-deficient conditions, roots were elongated and expanded with more secondary roots in search of P. The polymorphism information content for 13 gene-specific Markers associated with high Al and low P tolerance ranged from 2 to 4, with a mean of 2.31 per locus. Based on AMOVA, 90.85

Wild citrus species could be utilised as potential rootstocks in the citriculture industry. Seed germination, seedling characteristics, and their metrics are important traits of rootstocks and their evolutionary relationships. However, the limited information available affects the propagation and field performance of citrus. The experiment was conducted during winter (rabi) season of 2022 to 2024 at ICAR-Research Complex for NEH Region, Umiam, Meghalaya to understand the germination, growth behaviours and phylogenetic relationship of citrus rootstock species. Fifteen potential citrus rootstocks selected for the study were Citrus maxima, C. jambhiri, C. karna, C. latipes, C. limonia, C. aurantifolia, C. limon, C. macroptera, C. medica, C. paradisi, C. reshni, C. trifoliata, C. taiwanica, C. volkameriana, and C. indica. Results showed that the maximum germination traits, seedling growth and their metrics were obtained in C. jambhiri, C. latipes, C. limonia, C. maxima, and C. volkameriana. The highest chlorophyll index was recorded in C. limon (80.8 ± 3.7) and C. medica. Germination was strongly correlated with the germination speed index (r = 0.746**), mean daily germination (r = 0.845**), peak (r = 0.512**), and germination value (r = 0.596**). Principal component analysis revealed the presence of a wider variability for germination and seedling traits, with the first five components (eigen value>1) contributing 78.69% of the total variation. Phylogenetic analysis revealed that cluster I was monogenotypic (C. trifoliata) and cluster II comprised commonly used rootstocks, indicating a close relationship between them. Therefore, C. jambhiri, C. limonia, C. maxima, and C. latipes exhibited higher performance in germination behaviours, growth, and vigour of seedlings. Germination percentage and germination metrics could be important selection criteria for the improvement and utilisation of these species in propagation.

Introduction:The eastern Himalayan region of India with diverse agro-climatic conditions is one of the important hotspots of the world's biodiversity. A wide range of genetic variability of plant species like Colocasia is available in the region which is consumed by the local tribes. Materials and methods:A field study was conducted during 2022-23 to evaluate the yield, biochemical, mineral, and an-tioxidant parameters of 30 Colocasia esculenta L. Schott. genotypes under a split-plot design with three replications. Results and discussion:Significant (p < 0.05) variations were observed among genotypes for all traits. Tamachongkham exhibited the highest corm weight and yield, while Tamitin recorded the maximum cormel weight and total yield. Megha Taro-2 and Megha Taro-1 had the highest cormel numbers and cormel yield, respectively. In mineral composition, Tamitin had the highest N, K, Zn, Cu, and Mn, Tagitung White recorded the highest P, and BCC-2 had the highest Fe and Ca + Mg. Biochemically, Tamachongkham had the highest dry matter content; Khweng-2 had the highest starch, total sugar, and reducing sugar; Rengama had the highest crude protein, and crude fiber; and Mairang Local had the highest ash content. A significant positive correlation was observed between total yield and corm, cormel yield, cormel weight, and corm weight, while correlations with starch and other parameters were non-significant. Total phenolic content and anthocyanin were significantly correlated with Ferric Reducing Antioxidant Power (FRAP). Genotype-by-trait biplot analysis using the first two principal components (PC1: 19.4%, PC2: 14%) high-lighted total sugar, reducing sugar, cormel numbers, crude fiber, anthocyanin, and FRAP as major contributors to phenotypic diversity. The observed variations indicate the potential of these genotypes for future breeding programs aimed at improving taro production in the Eastern Himalayas.

Teasel gourd is an important, indigenous, vegetatively propagated, high-value, underutilized cucurbit vegetable crop grown in South and Southeast Asia. Due to its wider adaptability, it is grown from plains to mid-hills. The crop is lacking in research, primarily related to the extent of genetic diversity in the region and crop improvement, which is further constrained by dioecism. To assess the genetic diversity in male and female populations of teasel gourd based on morphological traits and microsatellite makers and the response of AgNO3 to induce monocliny, seventy genotypes, including eight males, were collected from different regions of the Northeastern states of India. Under evaluation trials, wider variability was observed for leaf, flower, and fruit characteristics. Traits: ovary length ranged from 0.58 to 1.23 cm, fruit length 4.76 to 11.23 cm, fruit diameter 3.0 to 3.13 cm, fruit weight 22.8 to 129.3 g, and 100 seed weights 12.60 to 36.3 g, reducing sugar 2.99 to 7.39

The application of 1D Convolutional Neural Networks (CNNs) for nutritional profiling using Near-Infrared Spectroscopy (NIRS) data has increased significantly in recent times. The accuracy of 1D CNNs depends on hyperparameters, yet a thorough investigation into their impact on model performance is lacking. Therefore, in this study, we explored the effects of hyperparameters on the performance of 1D CNN models for predicting nutritional traits using NIRS data from perilla, lablab bean, and rice bean crops. We tested a range of hyperparameters, including convolutional layers, filters, kernel sizes, pooling methods, batch sizes, activation functions, and optimizers. Our results show that increasing the number of convolutional layers improved the model's predictive power by enhancing feature extraction; however, beyond a certain limit, performance declined due to overfitting. Similarly, increasing the number of filters enhanced performance, but the optimal number needs to be decided to mitigate underfitting. Moderate kernel sizes struck a balance between feature preservation and generalization, while larger kernels decreased accuracy. Max pooling with smaller sizes provided optimal results by retaining essential features, whereas larger pooling sizes caused information loss. Smaller batch sizes were more effective at improving generalization, while larger batch sizes led to over-smoothing. LeakyReLU outperformed standard ReLU by avoiding the problem of dead neurons. And, a lower learning rate combined with the Adam optimizer resulted in smoother convergence and higher accuracy. Thus, the present study provides a framework for selecting hyperparameters in 1D CNN models to achieve optimal performance in nutritional trait estimation using NIRS data.

The north-eastern region of India, being considered part of the Indo-Malayan biodiversity hotspot, suffered from the extinction of a large number of landraces including rice (Oryza sativa L.). Recent changes, specifically rapid urbanization, extreme climate events, and the introduction of profitable agriculture using high-yielding varieties, have also supplemented the process. Systematic evaluation of diverse genotypes is urgently required for their proper utilization. By evaluating 148 rice genotypes from the north-eastern hilly region of India over three successive years, key traits-namely, the number of filled grains per panicle, test weight, and yield per plant were identified. These traits were found to be governed by additive gene action, with lesser environmental effects on their expression. Based on the multi-trait stability index and GGE (genotype main effect plus genotype by environment interaction) biplot analysis, eight genotypes, namely, Beoidhan 2, Jalbudi, Motadhan, Salidhan, Tapolea, Lypyagopal, Badalsali, and Dagum, were identified. The first five principal components cumulatively explained 79.51% of the total variance. Genotyping of rice germplasm using 50 Generation Challenge Programme markers resulted in a total of 94 alleles. Polymorphic information content ranged from 0.14 to 0.69 with an average of 0.36. Likewise, Shannon's information index ranged from 0.20 to 1.33, with an average of 0.57. Nei's genetic distance-based clustering has grouped the genotypes into four major clusters, whereas the Bayesian model-based approach has resulted in two groups with 142 pure lines and 06 admixtures. Based on an analysis of molecular variance, 57.63% of the variance was due to genetic differentiation among the individuals within populations, while 41.92% of the variance was accounted for within individuals. Wright's statistics indicated that the genotypes of Sikkim were highly differentiated from those of Tripura. The second-highest level of differentiation was observed among the genotypes of Mizoram and Tripura. In principal coordinate analysis, the first three axes explained 25.79% of the total variation. The landraces with a high level of differentiation and enhanced yield potential in multi-environments, namely, Motadhan, Salidhan, Tapolea, and Lypyagopal, would help increase the yield potential vis-& agrave;-vis farmers' income.

Buckwheat is an underutilized crop with high nutritional values and antioxidant properties. The study was conducted with an aim to assess the genetic variability of 37 Fagopyrum esculentum Moench. and 67 Fagopyrum tataricum Gaertn. accessions using agro-morphological, biochemical, and microsatellite markers. For yield per plant, accessions, namely, IC24297, IC26591, and IC13413, with superior performances were identified. Based on correlations and regression-based path analysis, the key component traits, namely, number of seeds per plant, 100-seed weight, leaf blade width, seed width, and cyme length, were identified. Among F. esculentum group, EC18771, IC107238, and IC107265 had the highest carbohydrate, protein, and the lowest phytic acid, respectively. Likewise, IC24297, EC18769, and IC26596, with the highest carbohydrate, protein, and lowest phytic acid content, respectively, were identified under the F. tataricum group. By analyzing 65 SSR markers, an average of 2.63 alleles per locus was detected, with an average PIC value of 0.431. The observed heterozygosity for the polymorphic markers varied from 0.019 (Fagopyrum tataricum) to 0.462 (Fagopyrum esculentum), with an average of 0.099, showing the lower level of expected heterozygosity in tartary buckwheat accessions. Based on Nei's genetic distances, the buckwheat accessions were grouped into three clusters. Clusters I and III included the tartary accessions, while Cluster II encompassed both species. The AMOVA, conducted by categorizing the accessions into 25 subpopulations, indicated that 80% of the observed variation was due to differences among individuals, whereas 19% was due to within individuals. Based on additive main effects and multiplicative interaction (AMMI) and multi-trait stability index (MTSI) analyses, accessions, namely, IC13141, IC49667, IC26587, IC107983, and IC107981, have been identified as the best accessions, exhibiting high mean yield and stability across all three environments, and could be utilized in augmenting the buckwheat cultivation.

Dragon fruit has evolved into a highly valuable commercial fruit crop worldwide, and the demand for quality planting materials (QPMs) has grown dramatically. However, limited availability of QPMs hinders its expansion. Therefore, experiments were conducted with the objective of identifying the optimum concentration of rooting hormones and formulating of lightweight growing mixtures, as well as assessing their propagation and nutrient uptake efficiency (NUE) in dragon fruit. The formulated substrates had significant differences in physicochemical properties and nutrient contents, affecting the rooting and growth of cuttings. The formulated substrates (S4-soil + cocopeat + vermicompost, 1:1:1/v:v:v) significantly promoted early root initiation with higher rooting and survival of plantlets. A higher increase in nutrient accumulation in the rooted cuttings (roots by 111

A study was carried out during 2022 and 2023 at ICAR-Research Complex for North-Eastern Hill Region, Umiam, Meghalaya to evaluate the yield, biochemical and antioxidant properties of 49 accessions of sweet potato [Ipomoea batatas (L.) Lam.] in mid hill condition of north-eastern region. The experiment was laid out in randomized block design (RBD) with 3 replications. Results indicate that Mynthlu Orange exhibited the highest tuber weight, length, diameter and yield. In terms of biochemical parameters, Meghalaya Local recorded the highest dry matter content; Col-6 recorded the highest starch content; and X-24 had the highest total sugar content. Among antioxidants, X-24 showed the highest total phenolic content, highest FRAP assay value and anthocyanin with lowest IC50 value which signified that X-24 had the highest antioxidant activity. Correlation study revealed significant positive correlation of tuber yield with tuber weight, diameter and total anthocyanin. Based on the mean performance, accessions Mynthlu Orange, X-24 and Col-6 were found promising for yield, biochemical and antioxidant parameters.

Lablab bean (Lablab purpureus L.), known for its higher protein content provides a promising alternative to reduce reliance on animal-based proteins and support sustainable agriculture. Nowadays, traditional methods for nutritional profiling have been outperformed by Convolutional Neural Networks (CNNs) integrated with Near-Infrared Reflectance Spectroscopy (NIRS). However, an advanced technique called Transformers, despite their potential, remains underexplored for prediction of key quality traits of crops. Thus, in the present study, we aimed to predict lablab bean protein content using a Transformer-based model (termed ProTformer) coupled with NIRS. The Dumas combustion method was used to determine protein content from 112 lablab bean genotypes. The performance of this model was compared with 1D CNN and Modified Partial Least Squares (MPLS)-based models. These models were evaluated using the Coefficient of Determination (RSQ), Residual Prediction Deviation (RPD), bias, and Corrected Standard Error of Prediction (SEP(C)). Our findings show that the Transformer-based model achieved the highest predictive accuracy (RSQ = 0.977, RPD = 13.92), surpassing the 1D CNN (RSQ = 0.956, RPD = 11.33) and MPLS (RSQ = 0.943, RPD = 10.53)- based models by 22.86% and 32.21% in RPD, respectively. The multi-head self-attention mechanism present in the Transformer-based model enables it to concurrently focus on different spectral regions, resulting in superior predictive performance. This study shows the potential of using Transformer-based models coupled with NIRS for nutritional profiling which could be effectively adapted for other crops for rapid screening of large germplasm available at global repositories.

Rice blast disease is one of the most disastrous diseases causing significant losses to the crop. In the humid weather conditions of north-eastern Himalayan region, the situation is highly devastating as the climate is very favorable to the fungus Magnaporthe oryzae. Development of resistant rice varieties is the most effective, economical, and environment-friendly way to control this disease. The study aimed to identify novel sources of resistant donor using agro-morphological and gene-based markers for their utilization in development of blast-resistant varieties with high yield potential. Phenotypic evaluation has classified the hundred landraces into resistant (13), moderately resistant (43), moderately susceptible (24), and susceptible (20). Fifty-nine genotypes were found to carry genes responsible for blast resistance, either singly or in combination. The genotype MN-62 was found to have a blast score of zero in field screening. The genetic frequencies of the major blast resistance genes ranged from 28 to 97

The study characterized 34 Job's tears accessions from the northeastern Himalayan region using yield-related traits and simple sequence repeats (SSR) markers. Genotyping with 17 SSR markers revealed an average of 3.18 alleles per locus, varying from 2 to 4. Polymorphism information content values ranged from 0.27 to 0.52, averaging 0.41. Clustering and principal coordinate analysis (PCoA) based on SSR markers grouped the accessions into three major groups. The first three principal coordinates in the PCoA cumulatively explained 41.96

Climate change especially the change in the temperature is having profound impacts on the pest population of tomato, a crucial commercial crop, in the Eastern Himalayan Region of India. To understand the impact of different thermal regimes on the fruit borer of tomatoes, field experiments were conducted at three locations with altitudes ranging from <500 to >1500 meters. At lower altitudes, fruit borer incidence was found to commence earlier in the season (5 th - 18 th March) and had a higher peak population (1.47 to 1.73 larvae/plant) causing more fruit damage (26-29%) as compared to the highest location (~9%). Correlation analysis indicated that maximum and minimum temperatures had significant positive impacts on the H. armigera incidence and fruit damage. Climatic datasets indicate an increase in the temperature of the region during the tomato growing season, thereby increasing the risk of fruit borer impact. As an adaptation option, we evaluated eight different tomato varieties/genotypes and studied biochemical parameters to understand their tolerance. Results showed a strong positive association of fruit borer incidence with total soluble solids and lycopene whereas negative association with acidity. Among the varieties/genotypes, Cherry tomato (7.62%) and MT-2 (10.04%) had relatively lower fruit damage; MT-3 (50.92 t/ha) and MT-2 (50.57 t/ha) consistently yielded the highest across all locations. Hence, the selection of appropriate genotypes and the development of varieties with suitable characteristics hold the key to fruit borer management. These findings can be valuable for pest management, understanding its further spread, and agricultural planning in the region.