AIMC Topic: Solanum lycopersicum

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Evaluation of conditional treatment effect of salt stress on tomato sugar content using causal machine learning: A pilot study.

PloS one
Exposing tomatoes to salt stress has been reported to increase the fruit sugar content (°Brix); however, the causal impact of this treatment under varying environmental conditions remains unclear. In this pilot study, a causal inference analysis was ...

Investigating cis-regulatory elements and gene expression in multiple tomato varieties using interpretable deep learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Cis-regulatory elements (CREs) govern gene expression, and the relationship between non-coding regulatory elements and gene expression is inherently complex. To further elucidate how these elements influence gene expression, we refined previous model...

Robotic cross-pollination of genetically modified flowers.

Science robotics
Engineered tomato plants produced flowers with visible stigmas that a robot could detect and pollinate faster than a human.

Explainable AI-driven interpretation of environmental drivers of tomato fruit expansion in smart greenhouses using IoT sensing.

Scientific reports
Tomato fruit expansion is a key physiological process that determines fruit size, marketability, and yield, yet its quantitative and threshold-based response to microclimatic factors in smart greenhouses has been insufficiently studied. This study de...

Detection of commercial crop weeds using machine learning algorithms.

Scientific reports
This work investigates the YOLOv5 object detection algorithms for classifying commercial crops such as tomatoes, chili, and cotton. The data sets comprise 707 images of green chillies, 200 images of tomato crops and 130 images of weeds from Ponnandag...

Machine learning-assisted aroma profile prediction in tomato puree based on flavoromics.

Food chemistry
Flavor serves as a key quality indicator in tomato puree (TP) processing; however, conventional methods often fall short in providing rapid and accurate assessments. To address this limitation, this study integrated flavoromics with machine learning ...

Interpretable deep multimodal-based tomato disease diagnosis and severity estimation.

Scientific reports
Plant diseases pose a significant threat to global food security, particularly in regions that rely heavily on crops that are vulnerable to disease, such as tomatoes. This research addresses the inefficiencies of traditional farming solutions by pres...

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...

Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction.

Scientific reports
Accurate scientific predicting of water requirements for protected agriculture crops is essential for informed irrigation management. The Penman-Monteith model, endorsed by the Food and Agriculture Organization of the United Nations (FAO), is current...

Tomato ripeness prediction using low resolution portable spectrometer and machine learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Tomato ripeness assessment is critical to ensure optimal product quality. This study proposes a novel approach to predict total soluble solids (TSS) and firmness, and classify tomato ripeness using a low-resolution AS7265x portable spectrometer combi...