Automatic and accurate estimation of disease severity is critical for disease management and yield loss prediction. Conventional disease severity estimation is performed using images with simple backgrounds, which is limited in practical applications. Thus, there is an urgent need to develop a method for estimating the disease severity of plants based on leaf images captured in field conditions, which is very challenging since the intensity of sunlight is constantly changing, and the image background is complicated. This study developed a simple and accurate image-based disease severity estimation method using an optimized neural network. A hybrid attention and transfer learning optimized semantic segmentation model was proposed to obtain the disease segmentation map. The severity was calculated by the ratio of lesion pixels to leaf pixels. The proposed method was validated using cucumber downy mildew, and powdery mildew leaves collected under natural conditions. The results showed that hybrid attention with the interaction of spatial attention and channel attention can extract fine lesion and leaf features, and transfer learning can further improve the segmentation accuracy of the model. The proposed method can accurately segment healthy leaves and lesions (MIoU = 81.23%, FWIoU = 91.89%). In addition, the severity of cucumber leaf disease was accurately estimated (R2 = 0.9578, RMSE = 1.1385). Moreover, the proposed model was compared with six different backbones and four semantic segmentation models. The results show that the proposed model outperforms the compared models under complex conditions, and can refine lesion segmentation and accurately estimate the disease severity. The proposed method was an efficient tool for disease severity estimation in field conditions. This study can facilitate the implementation of artificial intelligence for rapid disease severity estimation and control in agriculture.

Artificial intelligence (AI) akin to that used in facial-recognition software is accelerating the grape-breeding process by accurately identifying those individual vines that carry favorable genetic characteristics, notably those that provide mildew resistance and higher fruit quality. The robotic camera system developed at Cornell University, called Blackbird, could also help select parent breeding stock resistant to other pathogens and, as a more immediate benefit for growers, could be used to determine optimum fungicide combinations for different geographic localities. The Cornell-led, U.S. Department of Agriculture-funded VitisGen2 project uses a high-tech genetic sequencing approach, known as the rhAmpSeq system, to sift through the part of the genome that is common to all grapes and find DNA markers -- bits of genetic code -- that are associated with genetic traits of special interest to breeders. Even with these advances, technicians still had to spend hours hunched over microscopes, manually scanning small circular leaf samples for signs of powdery and downy mildew infection. This entailed clearing away the chlorophyll from the leaf tissue, staining each leaf disk so the mildew's filamentous and otherwise-transparent hyphae would show up, and assessing the presence and extent of the infection, said USDA research plant pathologist Lance Cadle-Davidson, who was part of the USDA-Cornell University team that developed rhAmpSeq for use on grape leaves.

The University of Florida has pioneered a method that uses artificial intelligence to find a disease early so growers who produce summer squash can keep it under control. Early detection gives farmers a fighting chance at a better crop. Summer and winter squash are grown commercially throughout the US state, particularly in southeast and southwest Florida. In 2019, Florida growers harvested 7,700 acres of squash, with a production value of US$35.4 million, according to the USDA National Agricultural Statistics Service. But powdery mildew disease, common throughout the world, can decrease yields.