Hyperspectral Imaging (HSI), which allows contact-less, non-destructive and fast optical sensing, is a good candidate for massive plant health monitoring in smart agriculture. To allow a dramatic reduction of both plant losses and chemical treatments, it is essential to provide the farmers with tools capable of detecting and identifying plant diseases, at very early stages, when the symptoms are not detectable by the human eye. Constructing such tools constitutes, today, an unaddressed challenge. In AGRIPOLHYS project we propose to perform early detection and identification of four tomato plant diseases (powdery mildew, late blight, gray mold and leaf mold), firstly, considering each disease separately, secondly, in a consortium. We propose to follow and combine three distinct paths: to use HSI system with an extended spectral range covering both VNIR (Visible Near Infrared) and SWIR (Short Wave Infrared); to add polarization sensing to the VNIR and SWIR HSI camera; to use innovative artificial intelligence algorithms. Firstly, AGRIPOLHYS project includes research efforts in new hardware HSI camera conception, for constructing a high-speed system capable of simultaneous polarization sensing and hyperspectral sensing. Secondly, AGRIPOLHYS project targets the improvement of the performances of the Artificial Intelligence algorithms realizing plant disease detection and identification. On one hand, we research new routes in Artificial Intelligence for the analysis of the colossal amount of data produced by the new high-speed spectro-polarimetric camera. On the other hand, we create a high-quality database containing ground truth by both imaging plant leaves and detecting plant diseases at early stages with new molecular and biochemical tools (as for example Volatile Organic Compounds). This database serves for algorithms calibration and testing.

The project deals with the urgent need to provide climate-resilient cultivars addressed to organic vegetable production systems. These new cultivars will benefit organic growers, and the organic seed industry, providing much needed security both under current and future scenarios of climate change. In this project, we will exploit the genetic variation of broccoli, kohlrabi, bean and tomato for enhanced productivity, by exploiting up-to-date knowledge of genome structure and function. This work will be enhanced by the active involvement of farmers, advisory services, research institutes, breeding companies and food processors from diverse geographical/climatic contexts in Europe and Non-EU countries. The selection of pre-breeding/ breeding lines for the three species will be undertaken in organic vegetable farming systems, utilizing an annual crop rotation scheme. New cultivars will be selected for efficiency when grown under water, temperature, and nitrogen stress, for resistance to some pests and diseases, for desirable product quality traits such as taste, visual appearance, post-harvest performance. The Stakeholder Board will contribute to the expected outcomes of the project. Crop genetic diversity will be broad as we will be utilizing several landraces (LRs) and crops wild relatives (CWRs) provided by partners for the foreseen pre-breeding and breeding activities. We will adopt an innovative approach, where plant traits related to the roots-zone, and to root-growth, and architecture that enable a better interaction with organic soil and its microbiome, are sought to benefit the end-users. This approach will ensure that the available genetic resources and bred-germplasm, combined with the best on-farm management practices will enhance resource use efficiency and productivity. The germplasm from this project will act to pump-prime the production of new seed for the organic growing sector and will also serve as a model for the enhancement of other crops.