The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models.

Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increase in population and climate change. Various crop genotypes can survive in harsh climatic conditions and give more production with less disease infection. Remote sensing can play an essential role in crop genotype identification using computer vision. In many studies, different objects, crops, and land cover classification is done successfully, while crop genotypes classification is still a gray area. Despite the importance of genotype identification for production planning, a significant method has yet to be developed to detect the genotypes varieties of crop yield using multispectral radiometer data. In this study, three genotypes of wheat crop (Aas-'2011', 'Miraj-'08', and 'Punjnad-1) fields are prepared for the investigation of multispectral radio meter band properties. Temporal data (every 15 days from the height of 10 feet covering 5 feet in the circle in one scan) is collected using an efficient multispectral Radio Meter (MSR5 five bands). Two hundred yield samples of each wheat genotype are acquired and manually labeled accordingly for the training of supervised machine learning models. To find the strength of features (five bands), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Nonlinear Discernment Analysis (NDA) are performed besides the machine learning models of the Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) with detailed of configuration settings. ANN and random forest algorithm have achieved approximately maximum accuracy of 97% and 96% on the test dataset. It is recommended that digital policymakers from the agriculture department can use ANN and RF to identify the different genotypes at farmer's fields and research centers. These findings can be used for precision identification and management of the crop specific genotypes for optimized resource use efficiency.

Unravelling the Morphological, Physiological, and Phytochemical Responses in Centella asiatica L. Urban to Incremental Salinity Stress.

Centella asiatica L. as a traditional medicinal plant is popular in several Asian countries and characterized by the presence of phytochemicals, such as phenolics and flavonoids. Soil salinity can affect the growth and phytochemical composition in this plant species. In this study, the effects of incremental soil salinity (0, 25, 50, 75, and 100 mM NaCl) on growth, physiological characteristics, total phenolic and total flavonoid contents, including the antioxidant activity of Centella asiatica L., were evaluated under greenhouse conditions. Salinity stress reduced growth, biomass production, and total chlorophyll contents, while increasing electrolyte leakage, Na+ and Cl- contents in the shoots and roots. With the increase of salt concentration, total phenolic, total flavonoid content and antioxidant activities were increased. The results showed that centella can tolerate saline conditions up to 100 mM NaCl. Na+ exclusion from the roots, and that increases of phytochemical content in the shoots were related to the salt tolerance of this species.