Morphological responses of three contrasting Soybean (Glycine max (L.) Merrill) genotypes under different levels of salinity stress in the coastal region of Bangladesh

Soil salinity, a global environmental issue, inhibits plant development and production. Soybean is an economically important legume crop whose yield and quality are highly affected by excessive levels of salt in the root zone. A factorial experiment was conducted in a net house from October 2019 to January 2020 to evaluate the performance of three distinct soybean genotypes under varying levels of salinity stress. The experiment followed a completely randomized design (CRD) with three replications. Three soybean cultivars, namely BINA Soybean 1, BINA Soybean 2, and BINA Soybean 4 were used in this experiment. The soil salinity treatments were 0 mM NaCl, 50 mM NaCl, 100 mM NaCl, 150 mM NaCl, and 200 mM NaCl. The electrical conductivity (EC) of the soil sample was 0.91dS/m. Six seeds were sown 3 cm deep in each pot. A total of 45 pots were used in this experiment. The performance of each variety was evaluated based on its germination percentage, time of germination, no. of branches/plant, no. of leaves/plant, no. of flowers/plant, plant height (cm), no. of pods/plant, pod length (cm), seeds/pod, and root length (cm). Based on the results obtained from this research trial, it can be inferred that the BINA Soybean 2 variety along with 0 mM NaCl, 50 mM NaCl, and 100 mM NaCl treatments exhibited superior performance in all parameters compared to the other varieties. This study provides clear evidence that the soybean, particularly the BINA Soybean 2 variety, holds significant promise as a crop suitable for coastal regions. Furthermore, it suggests that the cultivation of soybeans in such areas could potentially enhance agricultural productivity, particularly in the presence of mild saline conditions. Nevertheless, it exhibits limited growth potential in environments with elevated salinity levels

Machine learning based diabetes prediction and development of smart web application

Diabetes is a very common disease affecting individuals worldwide. Diabetes increases the risk of long-term complications including heart disease, and kidney failure among others. People might live longer and lead healthier lives if this disease is detected early. Different supervised machine learning models trained with appropriate datasets can aid in diagnosing the diabetes at the primary stage. The goal of this work is to find effective machine-learning-based classifier models for detecting diabetes in individuals utilizing clinical data. The machine learning algorithms to be trained with several datasets in this article include Decision tree (DT), Naive Bayes (NB), k-nearest neighbor (KNN), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR) and Support Vector Machine (SVM). We have applied efficient pre-processing techniques including label-encoding and normalization that improve the accuracy of the models. Further, using various feature selection approaches, we have identified and prioritized a number of risk factors. Extensive experiments have been conducted to analyze the performance of the model using two different datasets. Our model is compared with some recent study and the results show that the proposed model can provide better accuracy of 2.71% to 13.13% depending on the dataset and the adopted ML algorithm. Finally, a machine learning algorithm showing the highest accuracy is selected for further development. We integrate this model in a web application using python flask web development framework. The results of this study suggest that an appropriate preprocessing pipeline on clinical data and applying ML-based classification may predict diabetes accurately and efficiently