AIMC Topic: Plant Diseases

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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...

Application of deep learning for fruit defect recognition in Psidium guajava L.

Scientific reports
Psidium guajava L. is an important tropical and subtropical fruit. Due to its geographical location and suitable climate, Taiwan produces Psidium guajava L. all year round. Quality standardization is therefore a crucial issue. The primary objective w...

Identification of potent phytochemicals against Magnaporthe oryzae through machine learning aided-virtual screening and molecular dynamics simulation approach.

Computers in biology and medicine
Magnaporthe oryzae stands as a notorious fungal pathogen responsible for causing devastating blast disease in cereals, leading to substantial reductions in grain production. Despite the usage of chemical fungicides to combat the pathogen, their effec...

A text-speech multimodal Chinese named entity recognition model for crop diseases and pests.

Scientific reports
Named Entity Recognition for crop diseases and pests (NER-CDP) is significant in agricultural information extraction and offers vital data support for subsequent knowledge services and retrieval. However, existing NER-CDP methods rely heavily on plai...

SugarViT-Multi-objective regression of UAV images with Vision Transformers and Deep Label Distribution Learning demonstrated on disease severity prediction in sugar beet.

PloS one
Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval of large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, wee...

Autoregressive exogenous neural structures for synthetic datasets of olive disease control model with fractional Grünwald-Letnikov solver.

Computers in biology and medicine
A fundamental element of the Mediterranean diet, olive oil is abundant in heart-healthy monounsaturated fats and antioxidants, lowering the risk of cardiovascular diseases. However, the olive oil industry confronts hurdles arising from olive tree dis...

Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models.

Scientific reports
Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different...