AI Medical Compendium Journal:
Pest management science

Showing 1 to 10 of 33 articles

Multiple mutations in succinate dehydrogenase subunits conferring resistance to fungicide Pydiflumetofen in Magnaporthe oryzae.

Pest management science
BACKGROUND: Magnaporthe oryzae is the causal agent of rice blast disease. Pydiflumetofen, a novel succinate dehydrogenase inhibitor (SDHI), has promising potential for controlling rice blast owing to the antifungal activity of this fungicide.

Design and synthesis of novel cinnamamide derivatives-based lansiumamide B as potent fungicidal candidates.

Pest management science
BACKGROUND: Natural products have been and continue to be a significant resource for developing environmentally friendly pesticides with distinctive structures and mechanisms. Lansiumamide B (LB), a cinnamamide compound extracted from Clausena lansiu...

Discovery of novel benzoxazinone derivatives as promising protoporphyrinogen IX oxidase inhibitors.

Pest management science
BACKGROUND: Protoporphyrinogen IX oxidase (PPO, EC 1.3.3.4) has emerged as a key target for developing new herbicides to protect crops from weeds. Herein, we disclose the development of two types of PPO inhibitors by modification of the benzoxazinone...

Machine learning-based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs.

Pest management science
BACKGROUND: Insect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in...

Deep learning in disease vector image identification.

Pest management science
Vector-borne diseases (VBDs) represent a critical global public health concern, with approximately 80% of the world's population at risk of one or more VBD. Manual disease vector identification is time-consuming and expert-dependent, hindering diseas...

Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps.

Pest management science
BACKGROUND: The use of computer vision and deep learning models to automatically classify insect species on sticky traps has proven to be a cost- and time-efficient approach to pest monitoring. As different species are attracted to different colours,...

Monitoring of plant diseases caused by Fusarium commune and Rhizoctonia solani in bok choy using hyperspectral remote sensing and machine learning.

Pest management science
BACKGROUND: Local vegetable production is susceptible to various fungal pathogens, the most common and lethal of which are Fusarium commune and Rhizoctonia solani. Early detection of these pathogens is challenging, and by the time visual symptoms app...

Monitoring the leaf damage by the rice leafroller with deep learning and ultra-light UAV.

Pest management science
BACKGROUND: Rice leafroller is a serious threat to the production of rice. Monitoring the damage caused by rice leafroller is essential for effective pest management. Owing to limitations in collecting decent quality images and high-performing identi...

Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop-specific approaches.

Pest management science
BACKGROUND: Crop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automate...

Causality-inspired crop pest recognition based on Decoupled Feature Learning.

Pest management science
BACKGROUND: Ensuring the efficient recognition and management of crop pests is crucial for maintaining the balance in global agricultural ecosystems and ecological harmony. Deep learning-based methods have shown promise in crop pest recognition. Howe...