AIMC Topic: Solanum tuberosum

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IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases.

Scientific reports
In modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs)...

A lightweight hybrid model for scalable and robust plant leaf disease classification.

Scientific reports
Plant leaf diseases significantly impact crop yield and quality, causing substantial economic loss and risking food security. Despite significant progress in the field of automated plant disease diagnosis, there are still several challenges that need...

An explainable vision transformer with transfer learning based efficient drought stress identification.

Plant molecular biology
Early detection of drought stress is critical for taking timely measures for reducing crop loss before the drought impact becomes irreversible. The subtle phenotypical and physiological changes in response to drought stress are captured by non-invasi...

Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models.

Scientific reports
Potato as a staple food, plays a crucial role in ensuring a sustainable food supply and mitigating poverty and malnutrition in various regions across the globe. India, specifically holding the second position in global potato production, plays a sign...

Synergistic application of artificial intelligence and response surface methodology for predicting and enhancing in vitro tuber production of potato (Solanum tuberosum).

PloS one
In vitro regeneration of potato tubers is highly significant in modern agriculture as it offers efficient propagation, genetic enhancement, and pathogen-free seed production. This study aimed to optimize in vitro tuberization by manipulating key vari...

Joint control and machine learning prediction of co-formation and kinetic profiles of typical hazardous Maillard reaction products by catechin treatment in air-fried potato chips.

Food chemistry
The Maillard reaction generates hazardous processing contaminants, including acrylamide (AA) and Nε-(carboxymethyl)lysine (CML), necessitating effective inhibitors. Here we use machine learning approaches to predict how catechin treatment reduces sim...

Mapping acrylamide content in potato chips using near-infrared hyperspectral imaging and chemometrics.

Food chemistry
This study investigated the potential of near-infrared hyperspectral imaging (NIR-HSI) for the prediction of acrylamide content in potato chips. A total of 300 tubers from two potato varieties (Agria and Jaerla) grown in two seasons and processed und...

Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data.

Sensors (Basel, Switzerland)
While past research has emphasized the importance of late blight infection detection and classification, anticipating the potato late blight infection is crucial from the economic point of view as it helps to significantly reduce the production cost....

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data.

BMC bioinformatics
BACKGROUND: The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potenti...

PotatoG-DKB: a potato gene-disease knowledge base mined from biological literature.

PeerJ
BACKGROUND: Potato is the fourth largest food crop in the world, but potato cultivation faces serious threats from various diseases and pests. Despite significant advancements in research on potato disease resistance, these findings are scattered acr...