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Agriculture

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Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research
Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore...

Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm.

Environmental science and pollution research international
Mizoram (India) is part of UNESCO's biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence, the land use land cover (LULC) pattern of the state is frequently changing. We have used Landsat 5 and...

The fluidic memristor as a collective phenomenon in elastohydrodynamic networks.

Nature communications
Fluid flow networks are ubiquitous and can be found in a broad range of contexts, from human-made systems such as water supply networks to living systems like animal and plant vasculature. In many cases, the elements forming these networks exhibit a ...

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture.

Environmental monitoring and assessment
Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monit...

A dataset for fine-grained seed recognition.

Scientific data
The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed...

Research on precise phenotype identification and growth prediction of lettuce based on deep learning.

Environmental research
In recent years, precision agriculture, driven by scientific monitoring, precise management, and efficient use of agricultural resources, has become the direction for future agricultural development. The precise identification and assessment of pheno...

Integrating portable NIR spectrometry with deep learning for accurate Estimation of crude protein in corn feed.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study investigates the challenges encountered in utilizing portable near-infrared (NIR) spectrometers in agriculture, specifically in developing predictive models with high accuracy and robust generalization abilities despite limited spectral re...

Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning.

Journal of the science of food and agriculture
BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (cha...

Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

New biotechnology
In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on...

Recent advances in artificial intelligence towards the sustainable future of agri-food industry.

Food chemistry
Artificial intelligence has the potential to alter the agricultural and food processing industries, with significant ramifications for sustainability and global food security. The integration of artificial intelligence in agriculture has witnessed a ...