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.

Betacyanin (betanin), total phenolics, vitamin C and antioxidant capacity (by Trolox-equivalent antioxidant capacity (TEAC) and oxygen radical absorbance capacity (ORAC) assays) were investigated in two differently colored cactus pear (Opuntia ficus-indica (L.) Mill.) genotypes, one with purple fruit and the other with orange fruit, from the Salento area, in Apulia (South Italy). In order to quantitate betanin in cactus pear fruit extracts (which is difficult by HPLC because of the presence of two isomers, betanin and isobetanin, and the lack of commercial standard with high purity), betanin was purified from Amaranthus retroflexus inflorescence, characterized by the presence of a single isomer. The purple cactus pear variety showed very high betanin content, with higher levels of phenolics, vitamin C, and antioxidant capacity (TEAC) than the orange variety. These findings confirm the potential for exploiting the autochthonous biodiversity of cactus pear fruits. In particular, the purple variety could be an interesting source of colored bioactive compounds which not only have coloring potential, but are also an excellent source of dietary antioxidant components which may have beneficial effects on consumers’ health.

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.

Background: Water stress during grain filling has a marked effect on grain yield, leading to a reduced endosperm cell number and thus sink capacity to accumulate dry matter. The bread wheat cultivar Chinese Spring (CS), a Chinese Spring terminal deletion line (CS_5AL-10) and the durum wheat cultivar Creso were subjected to transcriptional profiling after exposure to mild and severe drought stress at the grain filling stage to find evidences of differential stress responses associated to different wheat genome regions.Results: The transcriptome analysis of Creso, CS and its deletion line revealed 8,552 non redundant probe sets with different expression levels, mainly due to the comparisons between the two species. The drought treatments modified the expression of 3,056 probe sets. Besides a set of genes showing a similar drought response in Creso and CS, cluster analysis revealed several drought response features that can be associated to the different genomic structure of Creso, CS and CS_5AL-10. Some drought-related genes were expressed at lower level (or not expressed) in Creso (which lacks the D genome) or in the CS_5AL-10 deletion line compared to CS. The chromosome location of a set of these genes was confirmed by PCR-based mapping on the D genome (or the 5AL-10 region). Many clusters were characterized by different level of expression in Creso, CS and CS_AL-10, suggesting that the different genome organization of the three genotypes may affect plant adaptation to stress. Clusters with similar expression trend were grouped and functional classified to mine the biological mean of their activation or repression. Genes involved in ABA, proline, glycine-betaine and sorbitol pathways were found up-regulated by drought stress. Furthermore, the enhanced expression of a set of transposons and retrotransposons was detected in CS_5AL-10.Conclusion: Bread and durum wheat genotypes were characterized by a different physiological reaction to water stress and by a substantially different molecular response. The genome organization accounted for differences in the expression level of hundreds of genes located on the D genome or controlled by regulators located on the D genome. When a genomic stress (deletion of a chromosomal region) was combined with low water availability, a molecular response based on the activation of transposons and retrotransposons was observed.