INTRODUCTION Moringa (Moringa oleifera Lam.,) is an incredible plant to mankind, because of its pharmacognostical and nutritional properties (Fahey, 2005). Out of 13 known species, Moringa is the single genus of the family Moringaceae (Mahmood et al., 2010) and Moringa oleifera is the most exploited species among them. Other names includes Miracle tree, Never die or Nature gift, or Mother‘s best friend. All the plant parts of moringa has been utilized for various purposes; the leaves are considered as a nutritionally superior vegetable containing more beta-carotene than carrots, more protein than peas, more vitamin C than oranges, more calcium than milk, more potassium than bananas and more iron than spinach (Prabhakar and Hebbar 2008). Moringa leaf/powder is the second most exported moringa product, with the value of 1.1 billion US dollars, after moringa seeds (1.6 billion). Moringa leaf or powder is most commonly exported from India. The European Union leads the way in importing moringa leaf or powder (Moringa-Meet 2015). Despite the high demand, no moringa variety has been developed specifically for leaf biomass. Induced mutation using physical and chemical mutagens is one way to create genetic variation which results in new varieties varieties with better characteristics (Devi and Mullainathan 2012). In order to improve leaf yield and other polygenic characters, mutation breeding can be effectively utilized (Deepalakshmi and Kumar 2004). Mutation breeding is one of the most effective ways of inducing genetic variability available to the plant breeder (Muhammed et al., 2016). The mutation breeding helps to improve one or two characters without changing the rest of the genotype (Arunal et al., 2010). Artificial induction of mutation provides raw materials for the genetic improvement of economic crops (Adamu and Aliyu 2007) and also used to create genetic variability in quantitative traits of various crop plants within the shortest possible times (Aruldoss et al., 2015). To assist in selecting for work on yield improvement, attributes linked to yield should also be determined using correlation and path coefficient analysis. Despite the fact that correlation analysis shows the pattern of relationships between component qualities and yield, it also illustrates the overall influence of a particular attribute on yield rather than a cause-and-effect relation. The method of path coefficient analysis makes it easier to distinguish genotypic correlation into the direct and indirect effects of different characters on yield (Mahbub et al., 2015). The goal of the current study was to evaluate the genetic variability, correlation, and path coefficient analysis of the moringa M3 generation mutants for leaf biomass. MATERIALS AND METHODS The present experiment was undertaken at Department of Vegetable Science, Horticultural College and Research Institute, Periyakulam, TNAU during 2021-2022. The experiment material is mutated annual moringa PKM 1 and the seeds of each treatment from M2 generation was forwarded into M3 generation with the spacing of 2.0 m between rows and 1.5m between plants. A total set of four mutants (2-1&7-1 mutants from gamma rays (100Gy) and 35-1&35-2 mutants from EMS (0.15%) from M2 generation were evaluated for different morphological traits. In each mutant 20 plants were evaluated and Observations on morphological traits viz., shoot length (cm), number of secondary branches per tree, number of rachis per tree, height at first branching (cm), internodal length (cm), trunk girth (cm), height at first harvest (cm), dry leaf yield (g), leaf powder recovery (%) and fresh leaf yield (g) was recorded. The variability for different quantitative parameters was estimated as per procedure suggested by Panse and Sukhatme (1961), GCV and PCV as per Burton (1952) heritability and genetic advance as per Johnson et al. (1955). Correlation coefficient was worked out as per Panse and Sukhatme (1961) and path coefficient analysis was worked out according to formula given by Dewey and Lu (1959). RESULT AND DISCUSSION A. Mutant 2-1(100Gy) The results of the estimation of genetic variability indicated that the considerable variability for all the traits in this mutant (Table 1). The phenotypic co-efficient of variation values are slightly greater than genotypic coefficient of variation values for most of the traits. There was a close relationship between genotypic coefficient of variation and phenotypic coefficient of variation for most of the traits which indicate that there was very little effect of environment on their gene expression. In this mutant the higher estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for shoot length, height at first branching, internodal length, dry leaf yield and fresh leaf yield. This indicates that the variability existing in these traits is due to genetic makeup. These results were in accordance with the findings of Karunagar et al. (2018) in Moringa and Praseetha (2015) in okra. Moderate estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for number of secondary branches per tree, number of rachis per tree and leaf powder recovery percentage. Similar results reported by Chandra et al. (2013) in okra. Low estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for the trait trunk girth and height at first harvest. High heritability coupled with high genetic advance as percent of mean was observed for the traits viz., shoot length, number of rachis per tree, height at first branching, internodal length, dry leaf yield, leaf powder recovery (%) and fresh leaf yield. Similar results were observed by Das et al. (2012) in okra. Moderate heritability and moderate genetic advance as percent of mean were observed for number of secondary branches per tree and trunk girth. High genetic advance as percent of mean was observed for height at first harvest. High heritability accompanied with high genetic advance as percent of mean indicates the involvement of additive gene action, therefore selection may be effective at later generations. Simple correlation co-efficient of 10 characters in all possible combinations was calculated to know the relationship among the mutants. The fresh leaf yield per plant had significant and positive association with shoot length (0.603), height at first branching (0.445), internodal length (0.642), height at first harvest (0.811), dry leaf yield (0.931) and leaf powder recovery percentage (0.809) (Table 5). Path coefficient analysis results revealed that the positive and high direct effect for number of secondary branches per tree (0.4014), height at first branching (0.3067), trunk girth (0.3691) and leaf powder recovery percentage (0.3685). These traits contributed the most towards fresh leaf yield per plant. Positive and low direct effect was observed only for this trait height at first harvest. The shoot length (-0.4640) and dry leaf yield (-0.7609) was registered negative and high direct effect. Negative and negligible direct effect for number of rachis per tree was observed in this mutant. Based on the path coefficient analysis number of secondary branches per tree, height at first branching, trunk girth and leaf powder recovery (%) may be considered as selection indices for yield improvement. B. Mutant 7-1 (100Gy) The results of the estimation of genetic variability indicated that the considerable variability for all the traits in this mutant (Table 2). The higher estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for the traits height at first branching, internodal length, dry leaf yield and fresh leaf yield. Similar findings was reported by Meena et al. (2012) in cabbage. The moderate estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for number of secondary branches per tree , number of rachis per tree, trunk girth and leaf powder recovery (%). Similar results were reported by Adiger et al. (2011) in moringa. The low estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for shoot length and height at first harvest. High heritability couple with high genetic advance as percent of mean was observed for the traits viz., number of secondary branches per tree, number of rachis per tree, height at first branching, internodal length, trunk girth, dry leaf yield, leaf powder recovery (%) and fresh leaf yield. Similar result were reported by Selvakumari et al. (2013) in moringa. Low heritability and genetic advance as percent of mean was observed for shoot length and height at first harvest. The fresh leaf yield per plant had significant and positive association was observed for number of secondary branches per tree (0.699), no of rachis per tree (0.874), trunk girth (0.394), height at first harvest (0.510), dry leaf yield (0.989) and leaf powder recovery percentage (0.863) (Table 6). Path coefficient analysis results revealed that the positive and high direct effect for number of rachis per tree (0.9248), leaf powder recovery percentage (0.7814), number of secondary branches per tree (0.7337), height at first harvest (0.4779) and trunk girth (0.3909). These traits contributed for fresh leaf yield per plant. Further positive and low direct effect was observed for internodal length (0.1316). Negative and high direct effect for shoot length (-0.4485) and negative and low direct effect on dry leaf yield (-0.1174). In this mutant based on this number of rachis per tree, number of secondary branches per tree, height at first harvest, trunk girth and leaf powder recovery (%) may be considered as selection indices for leaf yield improvement (Table 10) C. Mutant 35-1 (0.15% EMS) The results of the estimation of genetic variability indicated that the considerable variability for all the traits in this mutant (Table 3). The higher estimates of genotypic coefficient of variation and phenotypic coefficient of variation were observed for height at first branching, internodal length and fresh leaf yield. Moderate estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for shoot length, number of secondary branches per tree, number of rachis per tree, trunk girth and dry leaf yield. Similar result were observed by Chandra et al., (2014) in okra. Lower estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for height at first harvest and leaf powder recovery (%). High heritability coupled with high genetic advance as percent of mean was observed for the traits viz., number of rachis per tree, height at first branching, internodal length, trunk girth, dry leaf yield and fresh leaf yield. Similar results were observed by Raja and Bagle (2008) in moringa. High heritability with moderate genetic advance as percent of mean was observed for shoot length, height at first harvest. Moderate heritability with moderate genetic advance as percent of mean was observed for the trait number of secondary branches per tree. Low heritability with low genetic advance as percent of mean was observed for leaf powder recovery (%). The fresh leaf yield per plant had significant and positive association was observed for number of secondary branches per tree (0.885), number of rachis per tree (0.766), height at first harvest (0.805) and dry leaf yield (0.951). These findings coincides with the findings of Roy et al. (2016) in moringa and Naresh et al. (2021) in dolichos bean (Table 7). Path coefficient analysis results revealed that the positive and high direct effect for height at first harvest (0.9057). This traits contribute the most towards fresh leaf yield per plant. Further, positive and moderate direct effects were observed for height at first branching (0.2540) and internodal length (0.2750). Positive and negligible direct effect for shoot length (0.0469), and trunk girth(0.0654). These traits indicated strong positive association with leaf yield. Negative and high direct effects for the traits viz., leaf powder recovery (-0.7272), fresh leaf yield (-0.6839) and number of secondary branches per tree. Negative and low direct effect for dry leaf yield (-0.1061). Negative and negligible direct effects for number of rachis per tree (-0.0165). Based on the results of path co-efficient analysis it was observed that the height at first harvest, height at first branching, internodal length and trunk girth may be considered as selection indices for leaf yield improvement (Table 11). D. Mutant 35-2 (0.15% EMS) The results of the estimation of genetic variability indicated that the considerable variability for all the traits in this mutant (Table 4). The higher estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for number of rachis per tree, height at first branching , dry leaf yield and fresh leaf yield. These findings coincides with the findings of Sheetal and Maurya (2015) in moringa. Moderate estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for shoot length, internodal length, trunk girth and leaf powder recovery (%). Similar results were observed by Adiger et al. (2011) in moringa. Lower estimates of genotypic coefficient of variation and phenotypic coefficient of variation was observed for number of secondary branches per tree and height at first harvest. High heritability coupled with high genetic advance as percent of mean was observed for the traits viz., shoot length, number of rachis per tree, height at first branching, internodal length, trunk girth, dry leaf yield, leaf powder recovery (%) and fresh leaf yield. Similar results were reported by Sheetal and Maurya, (2015) in moringa. High heritability with moderate genetic advance as percent of mean were observed for height at first harvest. Low heritability and genetic advance as percent of mean was observed for number of secondary branches per tree. The fresh leaf yield per plant had significant and positive association with number of rachis per tree (0.574), trunk girth (0.648) and dry leaf yield (0.992). Similar results were reported by Selvakumari and Ponnuswamy (2015) in moringa (Table 8). Path coefficient analysis results revealed that positive and high direct effect for number of secondary branches per tree (0.9200), trunk girth (0.5840) and number of rachis per tree (0.3226). These traits contributed the most towards fresh leaf yield per plant. Further, positive and low direct effect on height at first harvest (0.1683) and dry leaf yield (0.1456). These traits indicated a strong positive association with yield. Negative and moderate direct effect on height at first branching (-0.2193) and internodal length (-0.2320). Negative and low direct effect for shoot length (-0.0511) and fresh leaf yield (-0.1195). Negative and negligible direct effects on leaf powder recovery percentage (-0.0511). Based on this number of rachis per tree, trunk girth, internodal length and leaf powder recovery (%) may be considered as selection indices for leaf yield improvement.