Insights on Plant Disease Identification Market with LGNet Strategy

A research team has developed LGNet , a dual - branch network that combines convolutional neural networks ( CNNs ) and visual transformers ( VTs ) for plant disease identification . This innovative approach enhances disease sensing capabilities and offers the potential for the development of efficient and robust plant disease recognition models , which are crucial for improving agricultural production and ensuring crop safety in diverse environments.

The demand for reliable disease detection is increasing exponentially . Farmers need to make informed decisions and protect their crops against potential threats . HSAT has the ability to orchestrate the capture of thousands of pictures of crops , taken with mobile devices by people who live near the fields , which is known as ground truth . HSAT analyses these pictures to detect the risk of disease.

LGNet effectively fuses local and global features , achieving state - of - the - art recognition accuracies of 88.74 % on the AI Challenger 2018 dataset and 99.08 % on the self - collected corn disease dataset . Recent advancements in image processing and deep learning have improved plant disease recognition , yet existing methods using only CNNs or VTs fall short due to their limited feature perception.

Using geolocation data , each image is mapped and associated with corresponding satellite and weather data . With this information , HSAT can identify which crops are touched by a disease , build heat maps across entire countries and build large - scale predictive models of national risks . The accuracy of this analysis rests on the thoughtful use of ground truth data and automated analysis.

Safeguarding agricultural production is vital for economic growth , as plant diseases significantly threaten crop yields . The traditional methods of identifying plant diseases , which rely on the farmers ' experience , are time - consuming and inadequate for large - scale cultivation . A study published in Plant Phenomics on 21 Jun 2024 , proposes LGNet , a dual - branch network combining CNNs and VTs that enhances both local and global feature extraction , achieving state - of - the - art performance on major datasets.

The accuracy of each epoch . Credit : Plant Phenomics ( 2024 ) . A research team has developed LGNet , a dual - branch network that combines convolutional neural networks ( CNNs ) and visual transformers ( VTs ) for plant disease identification . LGNet effectively fuses local and global features , achieving state - of - the - art recognition accuracies of 88.74 % on the AI Challenger 2018 dataset and 99.08 % on the self - collected corn disease dataset.