AIMC Topic: Plant Diseases

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IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases.

Scientific reports
In modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs)...

Contrast limited adaptive histogram equalization (CLAHE) and colour difference histogram (CDH) feature merging capsule network (CCFMCapsNet) for complex image recognition.

PloS one
To enhance crop yield, detecting leaf diseases has become a crucial research focus. Deep learning and computer vision excel in digital image processing. Various techniques grounded in deep learning have been utilized for detecting plant leaf diseases...

Interpretable deep multimodal-based tomato disease diagnosis and severity estimation.

Scientific reports
Plant diseases pose a significant threat to global food security, particularly in regions that rely heavily on crops that are vulnerable to disease, such as tomatoes. This research addresses the inefficiencies of traditional farming solutions by pres...

Nondestructive VOC-Based Phenotyping Strategy for Assessing Brown Planthopper Resistance at the Adult Stage in Rice.

Journal of agricultural and food chemistry
The brown planthopper (BPH) is a major rice pest in Asia, with the most severe damage occurring at the adult stage in rice. Although breeding resistant varieties is key to pest control, current screening focuses mainly on seedlings using destructive,...

Medicinal plant leaf disease classification using optimal weighted features with dilated adaptive DenseNet and attention mechanism.

Scientific reports
The agriculture sector plays a pivotal role in the growth of the global economy, but remains highly susceptible to prediction errors, particularly in disease identification. To address the limitations of existing approaches, this study proposes a dee...

Differential responses of Cacao pathogens Colletotrichum gloeosporioides and Pestalotiopsis sp. to UVB 305 nm and UVC 275 nm.

Scientific reports
Sustainable control of microbial pathogens requires alternatives to chemical agents. However, the efficacy of physical methods like Ultraviolet-C (UVC) radiation is often inconsistent due to poorly understood, pathogen-specific resistance mechanisms....

Rapid and accurate detection of crop viruses by nano-electrochemical sensors.

Analytical methods : advancing methods and applications
This review aims to critically examine the development, capabilities, and future prospects of nano-electrochemical sensors as a next-generation solution for the rapid and accurate detection of crop viruses. The motivation stems from the urgent need t...

YOLO-DP: A detection model of fifteen common rice diseases and pests.

Scientific reports
During rice cultivation, common rice diseases and pests such as Rice blast, Bacterial blight, Brown-planthopper and Leaf-folder will significantly affect the yield and quality. The current model is limited to detecting rice diseases or pests alone, a...

A fusion transfer learning framework for intelligent pest recognition in sustainable agriculture.

Scientific reports
With the fast growth in the population of the world, there is a constantly increasing requirement for sustainable food supplies. Agriculture is the backbone of the global food supply, with vegetables and fruits being essential for a balanced intake. ...

Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture.

Scientific reports
Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different ty...