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Tomato disease identification with shallow convolutional neural networks

Trinh et al. | Mar 03, 2023

Tomato disease identification with shallow convolutional neural networks

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

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Effect Of SMC On The Growth Of Bean, Cherry Tomato And Roma Tomato Plant

Rao et al. | Sep 12, 2020

Effect Of SMC On The Growth Of Bean, Cherry Tomato And Roma Tomato Plant

Mushroom compost, also called Spent Mushroom Substrate or Spent Mushroom Compost (SMC), is suitable for a variety of plants. Previous research has found that the application of SMC will increase plant growth. However, it is unclear which exact proportions of SMC and soil will maximize tomato and bean plant growth. We showed that the hypothesized growth media with 30% SMC optimizes seed germination, plant height, number of leaves, and survival rate compared to other combinations of growth media. Our research suggests that SMC is a useful alternative for conventional fertilizers.

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A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

Ganesh et al. | Mar 20, 2022

A novel approach for early detection of Alzheimer’s disease using deep neural networks with magnetic resonance imaging

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.

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Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Sirohi et al. | Sep 25, 2022

Risk assessment modeling for childhood stunting using automated machine learning and demographic analysis

Over the last few decades, childhood stunting has persisted as a major global challenge. This study hypothesized that TPTO (Tree-based Pipeline Optimization Tool), an AutoML (automated machine learning) tool, would outperform all pre-existing machine learning models and reveal the positive impact of economic prosperity, strong familial traits, and resource attainability on reducing stunting risk. Feature correlation plots revealed that maternal height, wealth indicators, and parental education were universally important features for determining stunting outcomes approximately two years after birth. These results help inform future research by highlighting how demographic, familial, and socio-economic conditions influence stunting and providing medical professionals with a deployable risk assessment tool for predicting childhood stunting.

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The Non-Thermal Effect of UV-B Irradiation on Onion Growth

Nashnoush et al. | Jun 09, 2020

The Non-Thermal Effect of UV-B Irradiation on Onion Growth

UV-B radiation due to the depletion of ozone threatens plant life, potentially damaging ecosystems and dismantling food webs. Here, the impact of UV-B radiation on the physiology and morphology of Allum cepa, the common onion, was assessed. Mitosis vitality decreased, suggesting UV-B damage can influence the plant’s physiology.

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Reduce the harm of acid rain to plants by producing nitrogen fertilizer through neutralization

Xu et al. | Apr 25, 2023

Reduce the harm of acid rain to plants by producing nitrogen fertilizer through neutralization
Image credit: Ave Calvar Martinez, pexels.com

The phenomenon of dying trees and plants in areas affected by acid rain has become increasingly problematic in recent times. Is there any method to efficiently utilize the rainwater and reduce the harmfulness of acid rain or make it beneficial to plants? This study aimed to investigate the potential of neutralizing acid rainwater infiltrating the soil to increase soil pH, produce beneficial salts for plants, and support better plant growth. To test this hypothesis, precipitation samples were collected from six states in the U.S. in 2022, and the pH of the acid rain was measured to obtain a representative pH value for the country. Experiments were then conducted to simulate the neutralization of acid rain and the subsequent change in soil pH levels. To evaluate the effectiveness and feasibility of this method, cat grass was planted in pots of soil soaked with solutions mimicking acid rain, with control and experimental groups receiving neutralizing agents (ammonium hydroxide) or not. Plant growth was measured by analyzing the height of the plants. Results demonstrated that neutralizing agents were effective in improving soil pH levels and that the resulting salts produced were beneficial to the growth of the grass. The findings suggest that this method could be applied on a larger agricultural scale to reduce the harmful effects of acid rain and increase agricultural efficiency.

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The Effect of Varying Training on Neural Network Weights and Visualizations

Fountain et al. | Dec 04, 2019

The Effect of Varying Training on Neural Network Weights and Visualizations

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

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