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Fusarium head blight (FHB) is a plant disease caused by various species of the Fusarium fungus. One of the major concerns associated with Fusarium spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and animal health and leads to major economic losses. A reliable site-specific precise Fusarium spp. infection early warning model is, therefore, needed to ensure food and feed safety by the early detection of contamination hotspots, enabling effective and efficient fungicide applications, and providing FHB prevention management advice. Such precision farming techniques contribute to environmentally friendly production and sustainable agriculture. This study developed a predictive model, Sága, for on-site FHB detection in wheat using imaging spectroscopy and deep learning. Data were collected from an experimental field in 2021 including (1) an experimental field inoculated with Fusarium spp. (52.5 m × 3 m) and (2) a control field (52.5 m × 3 m) not inoculated with Fusarium spp. and sprayed with fungicides. Imaging spectroscopy data (hyperspectral images) were collected from both the experimental and control fields with the ground truth of Fusarium-infected ear and healthy ear, respectively. Deep learning approaches (pretrained YOLOv5 and DeepMAC on Global Wheat Head Detection (GWHD) dataset) were used to segment wheat ears and XGBoost was used to analyze the hyperspectral information related to the wheat ears and make predictions of Fusarium-infected wheat ear and healthy wheat ear. The results showed that deep learning methods can automatically detect and segment the ears of wheat by applying pretrained models. The predictive model can accurately detect infected areas in a wheat field, achieving mean accuracy and F1 scores exceeding 89%. The proposed model, Sága, could facilitate the early detection of Fusarium spp. to increase the fungicide use efficiency and limit mycotoxin contamination.

High-throughput phenotyping is playing an increasingly important role in many areas of agriculture. Breeders will use it to obtain values for the traits of interest so that they can estimate genetic value and select promising varieties; growers may be interested in having predictions of yield well in advance of the actual harvest. In most phenotyping applications, image analysis plays an important role, drastically reducing the dependence on manual labor while being non-destructive. An automatic phenotyping system combines a reliable acquisition system, a high-performance segmentation algorithm for detecting fruits in individual images, and a registration algorithm that brings the images (and the corresponding detected plants or plant components) into a coherent spatial reference frame. Recently, significant advances have been made in the fields of robotics, image registration, and especially image segmentation, which each individually have improved the prospect of developing a fully integrated automatic phenotyping system. However, so far no complete phenotyping systems have been reported for routine use in a production environment. This work catalogs the outstanding issues that remain to be resolved by describing a prototype phenotyping system for a production tomato greenhouse, for many reasons a challenging environment.