Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large
areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a …

Core Ideas A deep learning model identified plant disease in UAV images with 95%
accuracy. Transfer learning allowed for faster model optimization. This method detected …

Plant disease poses a serious threat to global food security. Accurate, high-throughput
methods of quantifying disease are needed by breeders to better develop resistant plant …

Objectives Automated detection and quantification of plant diseases would enable more
rapid gains in plant breeding and faster scouting of farmers' fields. However, it is difficult for a …

Computer vision models that can recognize plant diseases in the field would be valuable
tools for disease management and resistance breeding. Generating enough data to train …

The identification of maize leaf diseases will meet great challenges because of the
difficulties in extracting lesion features from the constant-changing environment, uneven …

Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying
the diseases during the growing season are crucial. Some of the current studies are based …

Plants can be affected by various diseases. As such, the early detection of crop diseases
plays an essential role in the farming industry. However, such detection requires extensive …

In recent times, maize diseases have become widespread globally, adversely impacting
agricultural productivity and causing significant financial losses. Recognizing these …

Globally, all the major crops are significantly affected by diseases every year, as manual
inspection across diverse fields is time-consuming, tedious, and requires expert knowledge …