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. The models namely Support vector machine, XGBoost, Random forest, KNN, and Decision Tree were trained using yields of individual data sets of 11 agricultural and 10 horticultural crops, as well as combined yield of both agri-horticultural crops. The results strongly suggest to evaluate individual data sets separately for each crop category rather than using combined the data sets of both categories for better predictions. Comparing the five ML models, the XGBoost demonstrated the highest level of accuracy. The precision rates of XGBoost for recommending agricultural crops, horticultural crops, and a combination of both were 99.09 % (AUC 1.0), 99.3 % (AUC 1.0), and 98.51 % (AUC 0.99), respectively. This non-intrusive method for generating crop recommendations in diverse environmental conditions holds the potential to provide valuable insights for the development of a user-friendly AI cloud-based interface. Such an interface would enable rapid decision-making for optimal fertilizer applications and the selection of suitable crops for cultivation at specific sites.

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. We meticulously trained VGG16, VGG19, DenseNet201, ResNet50V2, Xception, InceptionResNetV2, and InceptionV3 deep neural network models. This training utilized a dataset comprising 5878 images encompassing 30 medicinal species distributed among 20 families. Our approach involved two avenues: the utilization of public data (PI) and a blend of public and field data (PFI), the latter featuring intricate backgrounds. Our study elucidates the robustness of these models in accurately identifying and classifying both interfamily and interspecies variations. Despite variations in accuracy across diverse families and species, the models demonstrated adeptness in these classifications. Comparing the models, we unearthed a crucial insight: the Normalized leverage factor (γω) for DenseNet201 stands at 0.19, elevating it to the pinnacle position for PI with a remarkable 99.64 % accuracy and 98.31 % precision. In the PFI scenario, the same model achieves a γω of 0.15 with a commendable 97 % accuracy. These findings serve as a guiding beacon for shaping future application tools designed to automate medicinal plant identification at the user level.

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. The findings showed that salinity-induced land use changes seriously threatened ecosystem services, employment and livelihoods. Shrimp farming was found to have replaced the majority of agricultural and bare lands, which led to the poor locals losing their land. The increasing land transformation to shrimp ponds as a coping strategy with salinity was not reported to be a viable option as maximum marginal poor people were unable to run the capital-intensive shrimp aquaculture. Eventually, many rich people occupied the cropland for shrimp farming which forced the traditional farmers and fishermen to leave their job and sell their labor. Many of the traditional services derived from the ecosystems were drastically reduced or got lost. The ultimate effect on the traditional livelihoods of the communities increased vulnerability and reduced resilience. The findings could aid in formulating realistic policies and action for ensuring the future resilience of the community through an appropriate adaptation strategy, such as introducing salinity-tolerant crops and integrated farming to safeguard the interest of the poor farmers. Despite the geographical locality of the study, its implications are global given the identical salinity concerns in other emerging nations' coastal regions.