Human activities significantly contribute to worldwide spread of phytopathological adversities. Pathogen-related food losses are today responsible for a reduction in quantity and quality of yield and decrease value and financial returns. As a result, “early detection” in combination with “fast, accurate, and cheap” diagnostics have also become the new mantra in plant pathology, especially for emerging diseases or challenging pathogens that spread thanks to asymptomatic individuals with subtle initial symptoms but are then difficult to face. Furthermore, in a globalized market sensitive to epidemics, innovative tools suitable for field-use represent the new frontier with respect to diagnostic laboratories, ensuring that the instruments and techniques used are suitable for the operational contexts. In this framework, portable systems and interconnection with Internet of Things (IoT) play a pivotal role. Here we review innovative diagnostic methods based on nanotechnologies and new perspectives concerning information and communication technology (ICT) in agriculture, resulting in an improvement in agricultural and rural development and in the ability to revolutionize the concept of “preventive actions”, making the difference in fighting against phytopathogens, all over the world.

To shed light on trends about elimination of viruses from plants, a bibliographic research was conducted to identify thermotherapy, chemotherapy and tissue culture trials published from 1991 through 2010. Among woody plants, grapevine, apple and peach are the most frequent targets of sanitation protocols because their health status is strictly regulated. Even if thermotherapy represents the preferred method for the host, grapevine viruses can also be eliminated with chemotherapy and tissue culture; apple viruses respond to chemotherapy as well. Although a similar trend was reported among herbaceous plants, chemotherapy was the most frequently used technique in potato. With regard to virus, thermotherapy was successfully applied against viruses belonging to 13 families and an unassigned genus. Instead, chemotherapy and tissue culture techniques eradicated viruses belonging to fewer families (nine). An interpretation of thermotherapy effects considers the new metabolic “pathways” triggered by the natural antiviral response emitted by the infected plant, with particular reference to virus-induced gene silencing. With regard to chemotherapy, several groups of antiviral drugs belong to inosine monophosphate dehydrogenase inhibitors, S-adenosylhomocysteine hydrolase inhibitors, neuraminidase inhibitors. Tissue culture, usually adopted to regenerate plantlets in biotechnological breeding programs, represents the less used tool for eliminate viruses from plants.

We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but required a vast amount of data which was obtained via crowd sourcing such as the PlantVillage project. This approach has limited applicability when samples need to be tested with traditional methods (i.e. PCR) to avoid incorrect training input or for quarantine pests which manipulation is restricted. In this paper, we demonstrate that transfer learning can be leveraged when it is not possible to collect thousands of new leaf images. Transfer learning is the re-application of an already trained deep learner to a new problem. We present a novel algorithm for fusing data at different levels of abstraction to improve performance of the system. The algorithm discovers low-level features from raw data to automatically detect veins and colors that lead to symptomatic leaves. The experiment included images of 100 healthy leaves, 99 X. fastidiosa-positive leaves and 100 X. fastidiosa-negative leaves with symptoms related to other stress factors (i.e. abiotic factors such as water stress or others diseases). The program detects OQDS with a true positive rate of 98.60 ± 1.47% in testing, showing great potential for image analysis for this disease. Results were obtained with a convolutional neural network trained with the stochastic gradient descent method, and ten trials with a 75/25 split of training and testing data. This work shows potential for massive screening of plants with reduced diagnosis time and cost.

The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.

Fruit quality is certainly influenced by biotic and abiotic factors, and a main quality attribute is the external appearance of the fruit. Various possible agronomical approaches are able to regulate the fruit microenvironment and, consequently, improve fruit quality and market value. Among these, fruit bagging has recently become an integral part of fruits’ domestic and export markets in countries such as Japan, China, Korea Australia and the USA because it is a safe and eco-friendly technique to protect fruits from multiple stresses, preserving or improving the overall quality. Despite increasing global importance, the development of suitable bagging materials and, above all, their use in the field is quite laborious, so that serious efforts are required to enhance and standardize bagging material according to the need of the crops/fruits. This review provides information about the effects of bagging technique on the fruit aspect and texture, which are the main determinants of consumer choice.