Parameter estimates for fishes of the upper Paraná river floodplain and Itaipu reservoir (Brazil)

Estimates of the growth (K), natural mortality (M), consumption/biomass (Q/B) rate and trophic level (TL) for 35 species in the upper Paraná river floodplain and the Itaipu reservoir (interconnected ecosystems) are presented. A compilation of these biological statistics is made for comparison purposes and some general trends are briefly discussed

Escapes of non-native fish from flooded aquaculture facilities: the case of Paranapanema River, southern Brazil

Non-native species are a major driver of biodiversity loss. Aquaculture activities play a key role in introductions, including the escape of fishes from fish farm facilities. Here, the impact of flooding due to El Niño rains in 2015/2016 in the Lower and Middle Paranapanema River basin, southern Brazil, was investigated by evaluating fish escapes from 12 fish farms. The flooding resulted in the escape of approximately 1.14 million fishes into the river, encompassing 21 species and three hybrids. Non-native species were the most abundant escapees, especially Oreochormis niloticus and Coptodon rendalli (96% of all fish). Only seven native fishes were in the escapee fauna, comprising 1% of all fish. Large floods, coupled with inadequate biosecurity, thus resulted in considerable inputs of non-native fish into this already invaded system

Creating a digital platform with a deep neural network for detecting plant diseases using information technology

This article is devoted to the detection of plant diseases using a platform with a deep neural network using information technologies. The goal of the work is to create a publicly available platform for detecting plant diseases, which is based on a model of a deep neural network trained in 45 classes of 15 crops (apple, corn, blueberry, rice, cherry, grapes, peach, orange, bell pepper, potatoes, raspberries, soybeans, strawberries, tomato and tea). The use of digital image processing is proposed to detect diseases. The study of many plant species has shown that this method has a high potential for determining the yield and quality of plants and is superior to traditional methods. Based on the finished Plant Disease Expert image data set taken from Kaggle, an EfficientNetB3 model was created that showed impressive results in the average accuracy of determining plant diseases - 98.1%. The article is supplied with graphic materials and tables, as well as a detailed description of each stage of the study