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Objective: The aim of the study was to analyze qualitative and quantitative phytochemicals, evaluate in vitro antioxidant properties and determine the bioactive compounds in extracts of Dicranopteris linearis (Burm.f.) Underw. collected from Western Ghats of Kanyakumari district. Methods:The qualitative, quantitative phytochemical, and in vitro antioxidant analysis were performed using standard procedures. The bioactive compounds were analyzed using gas chromatography-mass spectrometry (GC-MS) instrument. Results:The qualitative phytochemical analysis studied in aqueous, acetone, chloroform, ethanol, and petroleum ether solvent extract showed acetone had strong positivity to express the 12 phytoconstituents studied except anthocyanin when compared to other solvent extracts. The quantitative phytochemistry revealed considerable amount of terpenoids (97.0±1.15 mg/g), tannins (30.8±0.44 mg tannic acid equivalents/gram), phenols (28.6±0.33 mg gallic acid equivalents/gram), and flavonoids (8.50±0.29 mg quercetin equivalent/g) in decreasing order of concentrations. The in vitro antioxidant activity of aqueous, ethanol, acetone, chloroform, and petroleum ether suggested that the extract of DL has prominent antioxidant prospective against various free radicals such as 2,2-diphenyl-1-picrylhydrazyl while butylated hydroxy toluene being the standard antioxidant used. The GC-MS analysis displayed the presence of 11 bioactive compounds each belonging to various categories of phytochemicals such as terpenoids, flavonoids, phenols, and fatty acid derivatives. Conclusion:The results indicate that D. linearis (Burm.f.) Underw. present in the Western Ghats of Kanyakumari is an effective scavenger of free radicals and has the potential to be used as a natural antioxidant which is attributed to the rich presence of secondary metabolites.

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A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model.

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