AIMC Topic: Plant Leaves

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Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.

Sensors (Basel, Switzerland) Mar 21, 2025
Tea ( L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan's annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and qual...
Plant Leaves Camellia sinensis Tea Remote Sensing Technology Algorithms Taiwan Machine Learning Photosynthesis Unmanned Aerial Devices
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Banana Leaves Imagery Dataset.

Scientific data Mar 21, 2025
In this work, we present a dataset of banana leaf imagery, both with and without diseases. The dataset consists of 11,767 images, categorized as follows: 3,339 healthy images, 3,496 images of leaves affected by Black Sigatoka and 4,932 images of leav...
Fusarium Plant Diseases Musa Tanzania Machine Learning Plant Leaves
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Efficient deep learning-based tomato leaf disease detection through global and local feature fusion.

BMC plant biology Mar 11, 2025
In the context of intelligent agriculture, tomato cultivation involves complex environments, where leaf occlusion and small disease areas significantly impede the performance of tomato leaf disease detection models. To address these challenges, this ...
Plant Leaves Solanum lycopersicum Plant Diseases Deep Learning
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On construction of data preprocessing for real-life SoyLeaf dataset & disease identification using Deep Learning Models.

Computational biology and chemistry Mar 8, 2025
The vast volumes of data are needed to train Deep Learning Models from scratch to identify illnesses in soybean leaves. However, there is still a lack of sufficient high-quality samples. To overcome this problem, we have developed the real-life SoyLe...
Glycine max Plant Leaves Plant Diseases Deep Learning
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Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging.

Food chemistry Mar 8, 2025
In this paper, a transfer stack denoising autoencoder (T-SDAE) algorithm is proposed to implement the migration of cadmium (Cd) prediction depth characteristic model of oilseed rape leaves in different silicon environments. Stacked denoising autoenco...
Spectroscopy, Near-Infrared Deep Learning Algorithms Hyperspectral Imaging Silicon Brassica napus Plant Leaves Cadmium
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Leveraging YOLO deep learning models to enhance plant disease identification.

Scientific reports Mar 7, 2025
Early automation in identifying plant diseases is crucial for the precise protection of crops. Plant diseases pose substantial risks to agriculture-dependent nations, often leading to notable crop losses and financial challenges, particularly in deve...
Plant Diseases Fragaria Plant Leaves Crops, Agricultural Deep Learning
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Hybrid feature optimized CNN for rice crop disease prediction.

Scientific reports Mar 6, 2025
The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Adva...
Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Crops, Agricultural Plant Diseases Plant Leaves Oryza
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Sugarcane leaf disease classification using deep neural network approach.

BMC plant biology Mar 4, 2025
OBJECTIVE: The objective is to develop a reliable deep learning (DL) based model that can accurately diagnose diseases. It seeks to address the challenges posed by the traditional approach of manually diagnosing diseases to enhance the control of dis...
Plant Diseases Neural Networks, Computer Plant Leaves Deep Learning Saccharum
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A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases.

Scientific reports Feb 27, 2025
Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Tradi...
Plant Diseases Image Processing, Computer-Assisted Algorithms Manihot Deep Learning Plant Leaves
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Unveiling the potential of Brachiaria ruziziensis: Comparative analysis of multivariate and machine learning models for biomass and NPK prediction using Vis-NIR-SWIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Feb 18, 2025
This study investigated the development and validation of predictive models for estimating foliar nitrogen (N), phosphorus (P), and potassium (K) contents, along with shoot dry mass (SDM) of Brachiaria ruziziensis L. The approach utilized Vis-NIR-SWI...
Brachiaria Plant Leaves Least-Squares Analysis Spectroscopy, Near-Infrared Support Vector Machine Phosphorus Multivariate Analysis Nitrogen Potassium Machine Learning Biomass
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