Gerassimos Peteinatos

Potential use of ground‐based sensor technologies for weed detection

Site-specific weed management is the part of precision agriculture (PA) that tries to effectively control weed infestations with the least economical and environmental burdens. This can be achieved with the aid of ground-based or near-range sensors in combination with decision rules and precise application technologies. Near-range sensor technologies, developed for mounting on a vehicle, have been emerging for PA applications during the last three decades. These technologies focus on identifying plants and measuring their physiological status with the aid of their spectral and morphological characteristics. Cameras, spectrometers, fluorometers and distance sensors are the most prominent sensors for PA applications. The objective of this article is to describe-ground based sensors that have the potential to be used for weed detection and measurement of weed infestation level. An overview of current sensor systems is presented, describing their concepts, results that have been achieved, already utilized commercial systems and problems that persist. A perspective for the development of these sensors is given.

Enhancement of micro Unmanned Aerial Vehicles for agricultural aerial sensor systems

Micro Unmanned Aerial Vehicles (UAV) are used in a variety of agricultural research applications. However, UAVs are often not designed to interact with their attached sensor devices. This circumstance leads to disadvantages in sensor data acquisition and leads to increased efforts in post-mission data processing. This paper introduces a real-time sensor data software framework, executed on an embedded low-cost computer to aggregate carrier and sensor device measurements. The proposed setup provides functionality to control sensor devices and to retrieve, fuse, log and broadcast sensor data on-the-fly. A prototype implementation proved its functionality in an example use case of above-field micro-climate mapping with low-cost temperature, humidity and image sensors.

Camera steered mechanical weed control in sugar beet, maize and soybean

In sugar beet, maize and soybean, weeds are usually controlled by herbicides uniformly applied across the whole field. Due to restrictions in herbicide use and negative side effects, mechanical weeding plays a major role in integrated weed management (IWM). In 2015 and 2016, eight field experiments were conducted to test the efficacy of an OEM Claas 3-D stereo camera® in combination with an Einböck Row-Guard® hoe for controlling weeds. Ducks-foot blades in the inter-row were combined with four different mechanical intra-row weeding elements in sugar beet, maize and soybean and a band sprayer in sugar beet. Average weed densities in the untreated control plots were from 12 to 153 plants m−2 with Chenopodium album, Polygonum convolvulus, Thlapsi arvense being the most abundant weed species. Camera steered hoeing resulted in 78% weed control efficacy compared to 65% using machine hoeing with manual guidance. Mechanical intra-row elements controlled up to 79% of the weeds in the crop rows. Those elements did not cause significant crop damage except for the treatment with a rotary harrow in maize in 2016. Weed control efficacy was highest in the herbicide treatments with almost 100% followed by herbicide band-applications combined with inter-row hoeing. Mechanical weed control treatments increased white sugar yield by 39%, maize biomass yield by 43% and soybean grain yield by 58% compared to the untreated control in both years. However, yield increase was again higher with chemical weed control. In conclusion, camera guided weed hoeing has improved efficacy and selectivity of mechanical weed control in sugar beet, maize and soybean.

A non-chemical system for online weed control.

Non-chemical weed control methods need to be directed towards a site-specific weeding approach, in order to be able to compete the conventional herbicide equivalents. A system for online weed control was developed. It automatically adjusts the tine angle of a harrow and creates different levels of intensity: from gentle to aggressive. Two experimental plots in a maize field were harrowed with two consecutive passes. The plots presented from low to high weed infestation levels. Discriminant capabilities of an ultrasonic sensor were used to determine the crop and weed variability of the field. A controlling unit used ultrasonic readings to adjust the tine angle, producing an appropriate harrowing intensity. Thus, areas with high crop and weed densities were more aggressively harrowed, while areas with lower densities were cultivated with a gentler treatment; areas with very low densities or without weeds were not treated. Although the weed development was relatively advanced and the soil surface was hard, the weed control achieved by the system reached an average of 51% (20%–91%), without causing significant crop damage as a result of harrowing. This system is proposed as a relatively low cost, online, and real-time automatic harrow that improves the weed control efficacy, reduces energy consumption, and avoids the usage of herbicide.

Improving the determination of plant characteristics by fusion of four different sensors

Many different sensors have been proposed to estimate plant status parameters like nutrition status, coverage or plant size. Such parameters are key factors for precise management. This study combined four different sensors in a field trial with spring barley and oil seed rape. The following commercial sensors were used: LiDAR, spectrometer, ultrasonic device, and a commercial opto-electronic device. Spectral indices were calculated from the spectrometer and opto-electronic devices, and plant height from the ultrasonic and LiDAR sensors. A robotic software framework was used for simultaneous measurements with multiple sensors. The fusion of features from multiple sensors permitted the estimation of health status parameters for sensitive plants in a herbicide stress trial.

Utilization of Chlorophyll Fluorescence Imaging Technology to Detect Plant Injury by Herbicides in Sugar Beet and Soybean

Sensor technologies are expedient tools for precision agriculture, aiming for yield protection while reducing operating costs. A portable sensor based on chlorophyll fluorescence imaging was used in greenhouse experiments to investigate the response of sugar beet and soybean cultivars to the application of herbicides. The sensor measured the maximum quantum efficacy yield in photosystem II (PS-II) (Fv/Fm). In sugar beet, the average Fv/Fm of 9 different cultivars 1 d after treatment of desmedipham plus phenmedipham plus ethofumesate plus lenacil was reduced by 56% compared to the nontreated control. In soybean, the application of metribuzin plus clomazone reduced Fv/Fm by 35% 9 d after application in 7 different cultivars. Sugar beets recovered within few days from herbicide stress while maximum quantum efficacy yield in PS-II of soybean cultivars was reduced up to 28 d. At the end of the experiment, approximately 30 d after treatment, biomass was reduced up to 77% in sugar beet and 92% in soybean. Chlorophyll fluorescence imaging is a useful diagnostic tool to quantify phytotoxicity of herbicides on crop cultivars directly after herbicide application, but does not correlate with biomass reduction. Nomenclature: Desmedipham; ethofumesate; flufenacet; lenacil; metamitron; metribuzin; phenmedipham; soybean, Glycine max (L.) Merr.; sugar beet, Beta vulgaris (L.) ssp. vulgaris.