Comprehensive stomata image dataset of Sundarbans Mangrove and Ratargul Swamp forest tree species in Bangladesh.

Plants' leaf stomata are crucial for various scientific research, including identifying species, studying ecology, conserving ecosystems, improving agriculture, and advancing the field of deep learning. This dataset, containing 1083 images, encompasses 11 species from two distinct locations in Bangladesh: nine from the Sundarbans mangrove forest and two from the Ratargul Swamp Forest. It is a valuable tool for refining machine learning algorithms that specialize in detecting stomata and categorizing species accurately.

Strategic insights for sustainable growth of mushroom farming industry in Bangladesh: A comprehensive evaluation using SWOT-AHP and TOPSIS frameworks.

Mushroom farming using agri-waste as substrates can offer a sustainable solution to the food security challenges of inadequate and imbalanced diets. Developing strategies to exploit the potential of the mushroom industry fully is yet to be explored in Bangladesh. We, thus, conducted this study to investigate the challenges and opportunities associated with mushroom farming, as well as the characteristics of farms and employees engaged in this industry.

Machine learning based recommendation of agricultural and horticultural crop farming in India under the regime of NPK, soil pH and three climatic variables.

Machine learning (ML) can make use of agricultural data related to crop yield under varying soil nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production. The aim of this study was to evaluate the efficacy of five distinct ML models for a dataset sourced from the Kaggle repository to generate practical recommendations for crop selection or determination of required nutrient(s) in a given site. The datasets contain information on NPK, soil pH, and three climatic variables: temperature, rainfall, and humidity.

Assessing deep convolutional neural network models and their comparative performance for automated medicinal plant identification from leaf images.

Medicinal plants have got notable attention in recent years in the field of pharmaceutical and drug research. The high demand of herbal medicine in the rural areas of developing countries and drug industries necessitates correct identification of the medicinal plant species which is challenging in absence of expert taxonomic knowledge. Against this backdrop, we attempted to assess the performance of seven advanced deep learning algorithms in the automated identification of the plants from their leaf images and to suggest the best model from a comparative study of the models.

Salinity hazard drives the alteration of occupation, land use and ecosystem service in the coastal areas: Evidence from the south-western coastal region of Bangladesh.

Understanding the salinity effects on the rural livelihood and ecosystems services are essential for policy implications and mitigations. Salinity-driven modulation in land use and land cover, community traditional occupations, and ecosystem service have been elucidated in the present investigation. The study was carried out in the south-western region of Bangladesh as a representative case using focus group discussions, questionnaire survey, and remote sensing techniques.