ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.

Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species. Therefore, the role of taxonomy in earthworm study is significant. The taxonomy of earthworms cannot reliably be established through morphological characteristics because the small and simple body plan of the earthworm does not have anatomical complex and highly specialized structures. Recently, molecular techniques have been adopted to accurately classify the earthworm species but these techniques are time-consuming and costly. To combat this issue, in this study, we propose a machine learning-based earthworm species identification model that uses digital images of earthworms. We performed a stringent performance evaluation not only through 10-fold cross-validation and on an external validation dataset but also in real settings by involving an experienced taxonomist. In all the evaluation settings, our proposed model has given state-of-the-art performance and justified its use to aid earthworm taxonomy studies. We made this model openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/ESIDE.

Efficiency of cow dung based vermi-compost on seed germination and plant growth parameters of Tagetes erectus (Marigold).

Vermi-composting is an environmental friendly and economic process to decompose organic waste. The objective of this study was to produce vermi-compost using E isenia fetida and to investigate the impact of vermi-compost (VC) and organic manure (cow dung) on seed germination, seedlings, and growth parameters of Tagetes erecta. Physio-chemical parameters of vermi-compost and organic manure were recorded. A potting experiment was designed, germination medium containing soil, sand, and various concentrations of vermi-composts. The composition of germinating media was: TO (Sand + Soil), TCC (Sand + Soil + Cow dung), 10% VC (Sand + Soil + 0.1 kg VC), 15% VC (Sand + Soil + 0.15 kg VC), 20% VC (Sand + Soil + 0.2 kg VC), 25% VC (Sand + Soil + 0.25 kg VC), 30% VC (Sand + Soil + 0.3 kg VC), and 35% VC (Sand + Soil + 0.35 kg VC). Seed germination, seedling, vegetative plant growth, and flowering parameters were evaluated in different germinating media. Pre and post-physio-chemical parameters of germination media were also recorded to check their stability and quality. Results showed that 20% VC was effective for the early initiation of seed germination (2.0 ± 0.0 days) and all growth parameters of marigold seedlings. The germination percentage at 20% VC was recorded as 87.5 ± 1.40 %. The best vegetative plant growth and flowering parameters of marigold plants were observed with 35% VC after transplantation. Findings showed that vermi-compost is the best-suited germination and growing media, which not only improved the soil health but also promoted seed germination and plant growth. Our study undoubtedly indicates that vermi-compost is a more encouraging and advantageous bio-fertilizer and can be used as a powerful and effective for immediate marigold production.