Assessment of Agroeconomic Indicators of Sesamum indicum L. as Influenced by Application of Boron at Different Levels and Plant Growth Stages

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R²). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R² in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R²: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R²: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

Biofortification of mungbean (Vigna radiata L. (Wilczek)) with boron, zinc and iron alters its grain yield and nutrition

Mungbean [Vigna radiata L. (Wilczek)] is considered as an extremely nutritious crop possessing a high level of micronutrients, but their low bioavailability in the crop leads to micronutrient malnutrition in humans. Therefore, the present study was conducted to investigate the potential of nutrients viz. boron (B), zinc (Zn) and iron (Fe) biofortification on productivity, nutrient concentration and uptake as well as the economics of mungbean cultivation. In the experiment, the various combinations of RDF with ZnSO₄.7H₂O (0.5%), FeSO₄.7H₂O (0.5%) and borax (0.1%) were applied to mungbean variety ML 2056. The combined foliar application of Zn, Fe and B was highly efficient in increasing the yield of grain as well as straw in mungbean exhibiting maximum values i.e. 944 kg ha^-1 and 6133 kg ha^-1, respectively. Similar results for B, Zn and Fe concentration in grain (27.3 mg kg^-1, 35.7 mg kg^-1 and 187.1 mg kg^-1, respectively) and straw (21.1 mg kg^-1, 18.6 mg kg^-1 and 376.1 mg kg^-1, respectively) of mungbean were observed. Also, uptake of Zn and Fe by grain (31.3 g ha^-1 and 164.4 g ha^-1, respectively), as well as straw (113.7 g ha^-1 and 2295.0 g ha^-1, respectively), was maximum for the above treatment. Whereas, the B uptake was found to enhance significantly through the combined application of B, Zn and Fe, where the values 24.0 g ha^-1 and 128.7 g ha^-1 corresponded to grain and straw, respectively. Thus, combined use of ZnSO₄.7H₂O (0.5%) + FeSO₄.7H₂O (0.5%) and borax (0.1%) significantly improved the yield outcomes, the concentration of B, Zn and Fe, uptake and economic returns of mungbean cultivation to alleviate the B, Zn and Fe deficiency.

Biofortification of oil quality, yield, and nutrient uptake in Indian mustard (Brassica juncea L.) by foliar application of boron and nitrogen

Indian mustard (Brassica juncea L.) is an essential oilseed crop that offers important nutrients to human beings. However, the concurrent micronutrient deficiencies including boron (B), sulfur (S), and nitrogen (N) could pose a significant threat to public health. Therefore, this study was conducted at the Punjab Agricultural University, Ludhiana, with nine treatments, i.e., T₁-Control (recommended NPK only), T₂- borax (0.5%) at flowering, T₃-borax (1.0%) at flowering,T₄- borax (0.5%) + urea (1.0%) at flowering,T₅-borax (1.0%) + urea (1.0%) at flowering, T₆-borax (0.5%) at flowering + capsule formation, T₇-borax (1.0%) at flowering + capsule formation, T₈-borax (0.5%) + urea (1.0%) at flowering + capsule formation, T₉-borax (1.0%) + urea (1.0%) at flowering + Capsule formation, replicated three times in a randomized block design for 2 years (2020-2021 and 2021-2022). The foliar application of borax (1.0%) + urea (1.0%) at the flowering and capsule formation stage (treatment T₉) was highly efficient in increasing food quality parameters such as crude fiber, total soluble solids (TSS), and protein content with maximum values of 3.77, 24.9, and 27.53%, respectively. Also, maximum yields of seed as well as stover for treatment T₉ were 1.376 and 6.625 kg ha^-1, respectively. Similarly, the results for B, S, and N concentrations in seed (27.71 mg kg^-1, 17.69 mg kg^-1, and 2.35%), as well as stover (25.92 mg kg^-1, 17.31 mg kg^-1, and 0.33%), were maximum in treatment T₉. Also, B, S, and N uptake by seed (38.18 g ha^-1, 24.40 g ha^-1, and 32.05 Kg ha^-1) and stover (172.55 g ha^-1, 115.44 g ha^-1, and 21.99 Kg ha^-1) were maximum for the treatment T₉ involving borax (1.0%) + urea (1.0%) at the flowering and capsule formation stage. Whereas, the concentration and uptake decreased in the treatments involving the sole application of borax and urea. Therefore, the application of borax (1.0%) and urea (1.0%) at the flowering and capsule formation stage significantly improved the quality parameters, seed and stover yield, nutrient concentration, and uptake over control and could be used to alleviate the B, S, and N deficiency in Indian mustard.

Enrichment of Zinc and Iron Micronutrients in Lentil (Lens culinaris Medik.) through Biofortification

Biofortification of pulse crops with Zn and Fe is a viable approach to combat their widespread deficiencies in humans. Lentil (Lens culinaris Medik.) is a widely consumed edible crop possessing a high level of Zn and Fe micronutrients. Thus, the present study was conducted to examine the influence of foliar application of Zn and Fe on productivity, concentration, uptake and the economics of lentil cultivation (LL 931). For this, different treatment combinations of ZnSO₄·7H₂O (0.5%) and FeSO₄·7H₂O (0.5%), along with the recommended dose of fertilizer (RDF), were applied to the lentil. The results of study reported that the combined foliar application of ZnSO₄·7H₂O (0.5%) + FeSO₄·7H₂O (0.5%) at pre-flowering (S1) and pod formation (S2) stages was most effective in enhancing grain and straw yield, Zn and Fe concentration, and uptake. However, the outcome of this treatment was statistically on par with the results obtained under the treatment ZnSO₄·7H₂O (0.5%) + FeSO₄·7H₂O (0.5%) at S1 stage. A single spray of ZnSO₄·7H₂O (0.5%) + FeSO₄·7H₂O (0.5%) at S1 stage enhanced the grain and straw yield up to 39.6% and 51.8%, respectively. Similarly, Zn and Fe concentrations showed enhancement in grain (10.9% and 20.4%, respectively) and straw (27.5% and 27.6% respectively) of the lentil. The increase in Zn and Fe uptake by grain was 54.8% and 68.0%, respectively, whereas uptake by straw was 93.6% and 93.7%, respectively. Also the benefit:cost was the highest (1.96) with application of ZnSO₄·7H₂O (0.5%) + FeSO₄·7H₂O (0.5%) at S1 stage. Conclusively, the combined use of ZnSO₄·7H₂O (0.5%) + FeSO₄·7H₂O (0.5%) at S1 stage can contribute significantly towards yield, Zn and Fe concentration, as well as uptake and the economic returns of lentil to remediate the Zn and Fe deficiency.