Smart Agriculture and AI

Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse

This study uses machine learning to predict cotton leafworm infestation in greenhouses, finding XGBoost achieves 84% accuracy by analyzing environmental data.

Root Anatomy based on Root Cross-Section Image Analysis with Deep Learning

The paper develops a fully automated method using Faster R-CNN to detect root anatomical features in cross-section images and estimates key measurements with high accuracy.

Learning from Synthetic Dataset for Crop Seed Instance Segmentation

The paper trains a Mask R-CNN model for crop seed instance segmentation using synthetic data, achieving high performance on real-world datasets for barley and other crops.

High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat

This paper uses deep learning to score wheat morphology and flowering time from field images, revealing novel genetic interactions and heritable flowering rates.

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures

RootNav 2.0 uses deep learning to automatically extract root architectures from plant images, achieving comparable accuracy to semi-automatic methods with significantly increased speed.

A deep learning-based approach for high-throughput hypocotyl phenotyping

A deep learning-based algorithm provides an adaptable tool for determining hypocotyl or coleoptile length across different plant species.

Affordable Remote Monitoring of Plant Growth and Facilities using Raspberry Pi Computers

The paper develops a low-cost system using Raspberry Pi computers to monitor growth chamber conditions, enabling automated data logging and remote alerts.

Prediction of a plant intracellular metabolite content class using image-based deep learning

The paper uses image-based deep learning to predict intracellular metabolite concentration classes in plants, achieving 70% accuracy for fruit images.

AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning

AirSurf-Lettuce analyzes aerial images to measure lettuce phenotypes and support precision agriculture decisions.

DeepStomata: Facial Recognition Technology for Automated Stomatal Aperture Measurement

DeepStomata automates stomatal aperture measurement using image segmentation, achieving accuracy comparable to manual methods with higher sensitivity and much faster processing.