AIMC Topic: Plant Leaves

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Banana Leaves Imagery Dataset.

Scientific data
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...

Efficient deep learning-based tomato leaf disease detection through global and local feature fusion.

BMC plant biology
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 ...

Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging.

Food chemistry
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...

Leveraging YOLO deep learning models to enhance plant disease identification.

Scientific reports
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...

Hybrid feature optimized CNN for rice crop disease prediction.

Scientific reports
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...

Sugarcane leaf disease classification using deep neural network approach.

BMC plant biology
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...

A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases.

Scientific reports
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...

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
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...

Extraction of polysaccharides from Camellia oleifera leaves by dual enzymes combined with deep eutectic solvents screened by ANN and COSMO-RS.

International journal of biological macromolecules
Camellia oleifera leaves were byproduct of the C. oleifera industry which was rich in polysaccharides. Deep eutectic solvent-dual enzyme system (DES-dEAE) was established to achieve the simultaneous hydrolysis reaction of dual enzymes and DES extract...

A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions.

Food research international (Ottawa, Ont.)
With the steady rise in tea production, the need for effective tea quality monitoring has become increasingly pressing. Traditional sensory evaluation and wet chemical detection methods are insufficient for real-time tea quality monitoring. As an eme...