AIMC Topic: Gossypium

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Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery.

Sensors (Basel, Switzerland)
The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approach...

Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network.

BMC bioinformatics
BACKGROUND: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, m...

Artificial intelligence and regression analysis for Cd(II) ion biosorption from aqueous solution by Gossypium barbadense waste.

Environmental science and pollution research international
In this study, batch biosorption experiments were conducted to determine the removal efficiency of Cd(II) ion from aqueous solutions by Gossypium barbadense waste. The biosorbent was characterized by Fourier transform infrared spectroscopy (FTIR) and...

Comparative Analysis of the Cytology and Transcriptomes of the Cytoplasmic Male Sterility Line H276A and Its Maintainer Line H276B of Cotton (Gossypium barbadense L.).

International journal of molecular sciences
In this study, the tetrad stage of microspore development in a new cotton ( L.) cytoplasmic male sterility (CMS) line, H276A, was identified using paraffin sections at the abortion stage. To explore the molecular mechanism underlying CMS in cotton, a...

Cotton genotypes selection through artificial neural networks.

Genetics and molecular research : GMR
Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explore...

Machine-learning approach facilitates prediction of whitefly spatiotemporal dynamics in a plant canopy.

Journal of economic entomology
Plant-specific insect scouting and prediction are still challenging in most crop systems. In this article, a machine-learning algorithm is proposed to predict populations during whiteflies (Bemisia tabaci, Hemiptera; Gennadius Aleyrodidae) scouting a...

Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm.

PloS one
Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep lear...

[Biotransformation and safety of cotton stalk alkaline peroxide mechanical pulping waste liquor].

Wei sheng wu xue bao = Acta microbiologica Sinica
OBJECTIVE: We studied the conditions of culturing Bacillus subtilis SY1 to treat cotton stalk alkaline peroxide mechanical pulping waste water, and evaluated afterwards the safety of its release to plants and animals.