Objectives. Sustainable management of plant diseases is an open challenge which has relevant economic and environmental impact. Optimal strategies rely on human expertise for field scouting under favourable conditions to assess the current presence and extent of disease symptoms. This labor-intensive task is complicated by the large field area to be scouted, combined with the millimeter-scale size of the early symptoms to be detected. In view of this, image-based detection of early disease symptoms is an attractive approach to automate this process, enabling a potential high throughput monitoring at sustainable costs. Methods. Deep learning has been successfully applied in various domains to obtain an automatic selection of the relevant image features by learning filters via a training procedure. Deep learning has recently entered also the domain of plant disease detection: following this idea, in this work we present a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data and we propose the use of autoencoder architectures to investigate two strategies for disease detection: i) clusterization of features in a compressed space; ii) anomaly detection. Results. The two proposed approaches have been assessed by quantitative indices. The clusterization approach is not fully capable by itself to provide accurate predictions but it does cater relevant information. Anomaly detection has instead a significant potential of resolution which could be further exploited as a prior for supervised architectures with a very limited number of labeled samples.

FINGER LAKES – A radical collaboration between a Cornell biologist and an engineer is supercharging efforts to protect grape crops. The technology they've developed, using robotics and artificial Intelligence (AI) to identify grape plants infected with a devastating fungus, will soon be available to researchers nationwide working on a wide array of plant and animal research. The biologist, Lance Cadle-Davidson, Ph.D. '03, an adjunct professor in the School of Integrative Plant Science (SIPS), is working to develop grape varieties that are more resistant to powdery mildew, but his lab's research was bottlenecked by the need to manually assess thousands of grape leaf samples for evidence of infection. Powdery mildew, a fungus that attacks many plants including wine and table grapes, leaves sickly white spores across leaves and fruit and costs grape growers worldwide billions of dollars annually in lost fruit and fungicide costs. Cadle-Davidson is also a research plant pathologist with the U.S. Department of Agriculture's Agricultural Research Service (USDA-ARS).

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.