Deep learning for Chilean native flora classification: a comparative analysis

The limited availability of information on Chilean native flora has resulted in a lack of knowledge among the general public, and the classification of these plants poses challenges without extensive expertise. This study evaluates the performance of several Deep Learning (DL) models, namely InceptionV3, VGG19, ResNet152, and MobileNetV2, in classifying images representing Chilean native flora. The models are pre-trained on Imagenet. A dataset containing 500 images for each of the 10 classes of native flowers in Chile was curated, resulting in a total of 5000 images. The DL models were applied to this dataset, and their performance was compared based on accuracy and other relevant metrics. The findings highlight the potential of DL models to accurately classify images of Chilean native flora. The results contribute to enhancing the understanding of these plant species and fostering awareness among the general public. Further improvements and applications of DL in ecology and biodiversity research are discussed.

Biodegradation of malathion, α- and β-endosulfan by bacterial strains isolated from agricultural soil in Veracruz, Mexico

The objective of this study was to evaluate the capacity of two bacterial strains isolated, cultivated, and purified from agricultural soils of Veracruz, Mexico, for biodegradation and mineralisation of malathion (diethyl 2-(dimethoxyphosphorothioyl) succinate) and α- and β-endosulfan (6,7,8,9,10,10-hexachloro-1,5,5a,6,9,9a-hexahydro-6-9-methano-2,4,3-benzodioxathiepine-3-oxide). The isolated bacterial strains were identified using biochemical and morphological characterization and the analysis of their 16S rDNA gene, as Enterobacter cloacae strain PMM16 (E1) and E. amnigenus strain XGL214 (M1). The E1 strain was able to degrade endosulfan, whereas the M1 strain was capable of degrading both pesticides. The E1 strain degraded 71.32% of α-endosulfan and 100% of β-endosulfan within 24 days. The absence of metabolites, such as endosulfan sulfate, endosulfan lactone, or endosulfan diol, would suggest degradation of endosulfan isomers through non-oxidative pathways. Malathion was completely eliminated by the M1 strain. The major metabolite was butanedioic acid. There was a time-dependent increase in bacterial biomass, typical of bacterial growth, correlated with the decrease in pesticide concentration. The CO₂ production also increased significantly with the addition of pesticides to the bacterial growth media, demonstrating that, under aerobic conditions, the bacteria utilized endosulfan and malathion as a carbon source. Here, two bacterial strains are shown to metabolize two toxic pesticides into non-toxic intermediates.