Field phenotyping conceptually can be divided in five pillars 1) traits of interest 2) sensors to measure these traits 3) positioning systems to allow high throughput measurements by the sensors 4) experimental sites and 5) environmental monitoring. In this paper we will focus on photosynthesis as trait of interest, measured by remote active fluorescence. The sensor presented is the Light Induced Fluorescence Transient (LIFT) instrument. The LIFT instrument is integrated in three positioning systems. First in an automatized rail based positioning system moving in x, y and z direction above 120 miniplots. Miniplots are large (0.8m2) soil-filled containers placed inside and outside the greenhouse. Second the sensor is mounted on a manual operated Field4cycle and third on a fully autonomously moving, engine driven, GPS steered cart, called FieldCop. Photosynthetic traits were quantified for major crop species across seasonal changes in environment, grown at elevated CO2, or at different irrigation regimes. The quantum efficiency of photosystem II (Fq’/Fm’ )was light dependent whereas the electron transport rate efficiency (Fr2’/Fm’) was temperature dependent. Mean values of these photosynthetic traits or their interaction with environment allowed for characterization of different phenotypes. The LIFT instrument combined with selected positioning system provides high throughput proximal sensing of novel photosynthetic traits.

Currently Phenotyping is primarily performed using two-dimensional imaging techniques. While this yields interesting data about a plant, a lot of information is lost using regular cameras. Since a plant is three-dimensional, the use of dedicated 3D-imaging sensors provides a much more complete insight into the phenotype of the plant. Different methods for 3D-data acquisition are available, each with their inherent advantages and disadvantages. These have to be addressed depending on the particular application.

In this paper we demonstrate a number of representative methods each with a distinct set of features which make them suitable for particular applications. Each method is presented along with example setups. Simple guidelines are shown to help the researcher select the best technology for a given application. Sample data is presented gained from various real life applications to help understand the data quality that can be expected using a particular method.

Plant phenotyping techniques are important to present the performance of a crop and it interaction with the environment. The phenotype information is important for plant breeders to analyze and understand the plant responses from the ambient conditions and the inputs offered for it. However, for conclusive analysis it is necessary a large number of individuals. Thus, phenotyping is the bottleneck of plant breeding, a consequence of the labor intensive and costly nature of the classical phenotyping. Consequently, efficient high throughput phenotyping (HPP) is needed. In this scenario, many studies have evaluated the use of sensors for the development of an efficient HPP. Therefore, the aim of this study was to develop a greenhouse structure for plant phenotyping and to test sensors in order to evaluate the advantages and disadvantages of it for plant phenotyping. A structure with three rails was developed for scanning two vases with soybean plants. A camera T3 Canon, a LMS-200 (LiDAR) and a Kinect version 1 (K1) were used to generate the 3D models of the plants. According to the results, the LiDAR sensor generated the point cloud with the other two sensors. On the other hand, Kinect and T3 RGB cameras are very affected by the ambient light. Moreover, the sun light limits outdoor uses of the K1 sensor because the infrared from the sun amess with the infrared pattern generated by K1 used to measure the depth distances. In terms of processing time consuming, the Structure From Motion 3D reconstruction is the most time consuming. In general, LiDAR creates a robust result but still is a more expensive sensor, K1 is not very suitable for field conditions (sun light exposure) and RGB cameras can be used all conditions but processing is computer intensive. 

In recent years, growers in the state are transitioning to new high yielding, pest and disease resistant cultivars. Such transition has created high demand for new tree fruit cultivars. Nursery growers have committed their incoming production of the next few years to meet such high demands. Though an opportunity, tree fruit nursery growers must grow and keep the pre-sold quantity of plants to supply the amount promised to the customers. Moreover, to keep the production economical amidst rising labor shortages, the nursery growers are looking at incorporating technological advances on the horizon. Also to insure the young nursery seedlings from adverse winter weather, growers need to accurately know the tree inventory grown in the actual field environment. Therefore, objective of this study was to develop and validate robust field grown apple nursery plant counting algorithm that is based only on elevation pixel values of small Unmanned Aerial System (UAS) based low altitude RGB imagery data. The nursery field images were obtained using small UAS operated at 30 m above the ground level. Image processing was performed in a Geographic Information System (GIS) software, where the pipeline was defined focusing on the isolation of apple plants based on thresholds of pixel height in circular regions along the crop line. In the first step the Digital Elevation Model (DEM) was processed in order to extract the Digital Terrain Model (DTM); the height of the plants was estimated according to the Crop Surface Model (CSM), which is the difference between the DEM and DTM. In the second step, the center lines of crop rows were extracted. As a third step, inside each row line generated were the points with a fixed spacing of 25 cm and buffered circular regions with a diameter of 50 cm. Those buffer areas were classified aiming following the logistic that “only the circles with maximum height higher than 23 cm can be counted as plants”. The proposed methodology presented satisfactory results, reaching an estimation with an accuracy of 95%.

Integrated nutrient management (INM) practices are becoming common under intensive agricultural systems in Chile. Practices include, the use of organic matter, in different sources, soil microbial inoculants, and the application of biostimulants, of different origin. Compared to the application of macronutrients, for example, the effects of these products on crops are rather modest and require lower experimental errors to be proven; besides, trials made at the field level, many times do not have true replications, and assignment of treatments is not random. Because of these reasons, most commonly, treatments effects cannot be proven, even though, visually, differences could be observed. To deal with this reality, precision agriculture tools and proper statistical techniques, usually those used in econometrics, that simulate ceteris paribus have been used. To compare different treatments, we have used regression with binary variables, controlling for ancillary variables such plant biomass and geographic position, and time, when this is relevant for the experiment. Besides we have corrected for spatial (and temporal) autocorrelation, using spatial lag or spatial autoregressive models. In all our experiments, field data was collected using systematic grid designs, with n>20 and an average intensity > 6 samples/ha. Plant vigor was estimated by NDVI using the active sensor OptRx (AgLeader Technologies) passed several times during the season. In the present work, results of several experiments in table grapes are presented. In all trials, plant biostimulants were applied and crop yield and quality were the response variable. Results have shown that the proposed methodology is useful to make better evaluations of field trials for INM practices and can be an excellent tool for companies wanting to evaluate their products at farmer´s fields.

Water scarcity due to climate change, drought, and rising water demands from non-agricultural sectors, is threatening food production. Innovations in irrigation water management are required to optimize agricultural water use in water stressed regions of the world, and this requires more refined techniques of irrigation scheduling. The present study tends to investigate the integration of multispectral and thermal data for mapping crop water stress for precision irrigation management of vegetable crops. Airborne campaigns were conducted on a commercial tomato farm in Leamington, Southern Ontario, in 2017 growing seasons to obtain multispectral and thermal images of plant canopy. Irrigation was scheduled by dividing the field into three (3) sections consisting of 100%, 70%, and 50% of full replenishment of water in the root zone to field capacity, in order to induce mild to severe water stress on the tomato plants. One hundred (100) plants were selected from each of the three sections of the field, using a systematic grid sampling technique, and were georeferenced for identification in the acquired images. Plant stress parameters including canopy temperature, stomatal conductance, leaf area index (LAI), relative water content, and equivalent water thickness were measured from the selected plants, concurrently with the airborne campaigns. This is an on-going research work, and advanced data and image analysis techniques would be used to integrate the multispectral and thermal data for mapping crop water stress, in order provide more precise information about the plant water status. The result would be used to develop a field based algorithm for implementing near real-time irrigation scheduling based on remotely sensed data, and provide irrigators with an advanced tool for decision making.

It is feasible to identify specific phenotypic criteria indicative of robust root architecture that can be implemented in canola breeding programs and for designing effective management practices. Yet, the roles of roots in N absorption (responsible for nitrogen use efficiency; NUE), root anchorage strength (involved in lodging resistance and yield stability) and associated genotypic variations are not well understood.  we have recently developed a non-destructive electrical capacitance measurement and a 2-dimensional digital imaging method for examining the dynamics of root system architecture. The applications and limitations of the capacitance measurement method for assessing the responses of canola crops to abiotic stress will be discussed.