Smart Agriculture and AI

Leveraging High-dimensional Seed Colorspace in Phenomic Selection Models for Herbaceous Perennial Crops

The paper uses seed color and morphology data from image scans to predict germination traits in perennial crops, showing improved prediction accuracy when combining HSV color features with morphology.

A user-friendly machine-learning program to quantify stomatal features from fluorescence images

The paper introduces QuickSpotter, a machine-learning tool for semi-automated stomatal annotation in fluorescence images, and demonstrates its use in analyzing stomatal morphology during development and under pharmacological treatments.

A Lightweight Deep Learning Architecture for Potato Leaf Disease Detection: A Comprehensive Survey

This paper evaluates lightweight CNN models for potato leaf disease classification, finding ShuffleNet offers the best performance while balancing efficiency and accuracy.

LeafyResNet: Fusarium Wilt Detection in Lettuce Using UAV RGB Imaging and Advanced Deep Learning Model

This paper develops LeafyResNet, a deep learning model for detecting Fusarium wilt in lettuce using UAV RGB imaging, achieving high accuracy and precision.

Video-Based Cattle Behavior Detection for Digital Twin Development in Precision Dairy Systems

The paper develops a video-based framework for detecting and classifying cattle behaviors to support digital twin systems in precision dairy farming.

pyRootHair: Machine Learning Accelerated Software for High-Throughput Phenotyping of Plant Root Hair Traits

pyRootHair automates root hair trait quantification from plant root images, revealing significant variation across wheat cultivars and supporting high-throughput analysis.

Impact of cross-validation designs on cattle behavior prediction using machine learning and deep learning models with tri-axial accelerometer data

Leave-cow-out validation lowered cattle behavior classification accuracy to 0.72 to 0.82, versus 0.9 to 0.96 under random hold-out splits that can place one animal's records in both sets.

Integrating Load-Cell Lysimetry and Machine Learning for Prediction of Daily Plant Transpiration

This study uses machine learning models trained on gravimetric and environmental data to predict daily plant transpiration with high accuracy.

Deep learning from videography as a tool for measuring E. coli infection in poultry

This study uses deep learning-based tracking to detect behavioral changes in infected poultry, showing reduced activity in E. coli-infected chickens compared to controls.

Multi-Model Machine Learning for Automated Identification of Rice Diseases Using Leaf Image Data

This paper develops a hybrid deep-machine learning system for automated rice disease detection using leaf images, achieving high accuracy and efficiency.

Botanic Spectrum Analyser: A Deep Learning GUI for Plant Image Segmentation in Hyperspectral and RGB Phenotyping

The paper introduces BSA, a deep learning GUI for plant image segmentation in RGB and hyperspectral imagery, achieving high accuracy across species and imaging modalities.

Wireless Sensor Network: New Concept of Spatial-Temporal Monitoring Plant-Environment Interactions

The paper introduces a wireless sensor network for continuous monitoring of plant-environment interactions, showing strong correlation between transpiration and biomass gain.

Synergistic 3D, multispectral, and thermal image analysis via supervised machine learning for improved detection of root rot symptoms in hydroponically-grown flat-leaf parsley

This study uses supervised machine learning to combine 3D, multispectral, and thermal imaging data for improved detection of root rot in hydroponic parsley.

MaiZaic: a robust end-to-end pipeline for mosaicking freely flown aerial video of agricultural fields

MaiZaic improves mosaic accuracy for UAV agricultural imagery by using deep learning for homography estimation and error reduction.

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset

A 1,096-image pixel-labelled wheat organ dataset plus 52,078 unlabelled field images from 11 institutions enables Segformer leaf and spike segmentation near 90% mIoU, while stems lag.

BeeSAM2: detecting bees in cherry flowers using timelapse images and foundational models

BeeSAM2 detects bumblebees in cherry flower timelapse images using foundational models, achieving high recall and precision for efficient pollinator monitoring.

Hyperspectral image analysis for classification of multiple infections in wheat

This study uses hyperspectral imaging and deep learning to classify multiple infections in wheat, achieving 81% accuracy for single and double infections.

RegenPGC View: Semantic Segmentation of Perennial Groundcover Cropping Systems to Restore American Croplands

This paper develops a deep learning framework for estimating perennial groundcover crop establishment using image data, improving objectivity and efficiency in agricultural research.

The Fengshu Large Model for Wugu Fengdeng: An Innovation Engine for Knowledge Integration in the Soybean Field

This paper introduces Fengshu-Agri, a large model integrating agricultural knowledge to improve soybean management, achieving high accuracy in knowledge retrieval and complex question answering.

Automated and high throughput measurement of leaf stomatal traits in canola

SCAN measures canola stomatal traits with 89-94% accuracy using YOLOv8 and digital microscopy, and predicts traits in other species.

PhenoVision: A framework for automating and delivering research-ready plant phenology data from field images

PhenoVision automates plant phenology data extraction from field images, achieving high accuracy in detecting flowers and fruits, and expands global phenology data coverage.

Analysis of Wheat Spike Morphological Traits Using 2D Imaging

This study develops SpikePheno, a deep learning pipeline for precise wheat spike phenotyping, revealing correlations between spike morphology and yield, and identifying geographical and temporal phenotypic trends.

FieldDino: High-throughput physio-morphological phenotyping of stomatal characteristics for plant breeding research

Handheld porometry, a 3D-printed leaf-clip microscope and YOLOv8 segmentation jointly phenotype stomatal conductance and anatomy on the same wheat leaf across 200 field genotypes.

Spatio-temporal modeling of host-pathogen interactions using level set method

This paper develops a spatio-temporal model for plant disease phenotyping using image processing and the level set method to track lesion progression and host-pathogen interactions.

Optimizing Soybean Breeding: High-Throughput Phenotyping Technologies for Stink Bug Resistance and High Yields

This study uses high-throughput phenotyping with UAVs and machine learning to assess soybean resistance to stink bugs and yield, finding that certain image features and models can predict traits but not resistance effectively.

DEEP LEARNING-BASED HIGH-THROUGHPUT PHENOTYPING OF MAIZE (Zea mays L.) TASSELING FROM UAS IMAGERY ACROSS ENVIRONMENTS

This paper develops a CNN-based method for high-throughput maize tasseling phenotyping using UAS imagery across multiple environments, achieving high classification accuracy.

LLS-SevEst - Late leaf spot severity estimator. A machine learning approach to assessing Nothopassalora personata in peanut.

This paper develops an automated image analysis model, LLS-SevEst, to quantify late leaf spot severity in peanut leaves using image segmentation techniques.

Open RGB Imaging Workflow for Morphological and Morphometric Analysis of Fruits using AI: A Case Study on Almonds.

The paper develops an open Python workflow using AI to analyze fruit morphology and morphometric traits in almonds, identifying new heritable traits for improved breeding strategies.

From Canopy Images to Organ-Level Disease Assessments: A Scalable Approach to Measure Quantitative Resistance in the Field

This paper introduces an imaging protocol and pipeline to measure wheat disease severity from canopy images, achieving high correlation with manual methods and enabling scalable, contact-free disease assessment.

Infrared thermography for plant stress detection in vertical farms: Investigating spatiotemporal variations and exploring solutions via machine learning

This study uses infrared thermography and machine learning to detect plant stress in vertical farms, finding that binary models trained on medial stress levels provide reliable probabilistic stress estimates despite spatiotemporal variations.