Browsing by Subject "Sensor technologies"

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Advances in site-specific weed management in agriculture: A review
(2022) Gerhards, Roland; Andújar Sanchez, Dionisio; Hamouz, Pavel; Peteinatos, Gerassimos G.; Christensen, Svend; Fernandez‐Quintanilla, Cesar
The developments of information and automation technologies have opened a new era for weed management to fit physical and chemical control treatments to the spatial and temporal heterogeneity of weed distributions in agricultural fields. This review describes the technologies of site‐specific weed management (SSWM) systems, evaluates their ecological and economic benefits and gives a perspective for the implementation in practical farming. Sensor technologies including 3D cameras, multispectral imaging and Artificial Intelligence (AI) for weed classification and computer‐based decision algorithms are described in combination with precise spraying and hoeing operations. Those treatments are targeted for patches of weeds or individual weed plants. Cameras can also guide inter‐row hoes precisely in the centre between two crop rows at much higher driving speed. Camera‐guided hoeing increased selectivity and weed control efficacy compared with manual steered hoeing. Robots combine those guiding systems with in‐row hoeing or spot spraying systems that can selectively control individual weeds within crop rows. Results with patch spraying show at least 50% saving of herbicides in various crops without causing additional costs for weed control in the following years. A challenge with these technologies is the interoperability of sensing and controllers. Most of the current SSWM technologies use their own IT protocols that do not allow connecting different sensors and implements. Plug & play standards for linking detection, decision making and weeding would improve the adoption of new SSWM technologies and reduce operational costs. An important impact of SSWM is the potential contribution to the EU‐Green Deal targets to reduce pesticide use and increase biodiversity. However, further on‐farm research is needed for integrating those technologies into agricultural practice.
A comparison of seven innovative robotic weeding systems and reference herbicide strategies in sugar beet (Beta vulgaris subsp. vulgaris L.) and rapeseed (Brassica napus L.)
(2023) Gerhards, Roland; Risser, Peter; Spaeth, Michael; Saile, Marcus; Peteinatos, Gerassimos
More than 40 weeding robots have become commercially available, with most restricted to use in crops or fallow applications. The machines differ in their sensor systems for navigation and weed/crop detection, weeding tools and degree of automation. We tested seven robotic weeding systems in sugar beet and winter oil‐seed rape in 2021 and 2022 at two locations in Southwestern Germany. Weed and crop density and working rate were measured. Robots were evaluated based on weed control efficacy (WCE), crop stand loss (CL), herbicide savings and treatment costs. All robots reduced weed density at least equal to the standard herbicide treatment. Band‐spraying and inter‐row hoeing with RTK‐GPS guidance achieved 75%–83% herbicide savings. When hoeing and band spraying were applied simultaneously in one pass, WCE was much lower (66%) compared to the same treatments in two separate passes with 95% WCE. Hoeing robots Farmdroid‐FD20®, Farming Revolution‐W4® and KULTi‐Select® (+finger weeder) controlled 92%–94% of the weeds. The integration of Amazone spot spraying® into the FD20 inter‐row and intra‐row hoeing system did not further increase WCE. All treatments caused less than 5% CL except for the W4‐robot with 40% CL and the combination of conventional inter‐row hoeing and harrowing (21% CL). KULT‐Vision Control® inter‐row hoeing with the automatic hydraulic side‐shift control resulted in 80% WCE with only 2% CL. Due to the low driving speed of maximum 1 km h−1 of hoeing robots with in‐row elements, treatment costs were high at 555–804 € ha−1 compared to camera‐guided inter‐row hoeing at 221 € ha−1 and broadcast herbicide application at 307–383 € ha−1. Even though the costs of robotic weed management are still high, this study shows that robotic weeding has become a robust, and effective weed control method with great potential to save herbicides in arable and vegetable crops.
Development of a robust sensor calibration for a commercially available rising platemeter to estimate herbage mass on temperate seminatural pastures
(2024) Werner, Jessica; Salazar‐Cubillas, Khaterine; Perdana-Decker, Sari; Obermeyer, Kilian; Velasco, Elizabeth; Hart, Leonie; Dickhöfer, Uta
Rising platemeters are commonly used in Ireland and New Zealand for managing intensive pastures. To assess the applicability of a commercial rising platemeter operating with a microsonic sensor to estimate herbage mass with its own equation, the objectives were (i) to validate the original equation; (ii) to identify possible factors hampering its accuracy and precision; and (iii) to develop a new equation for heterogeneous swards. A comprehensive dataset (n = 1511) was compiled on the pastures of dairy farms. Compressed sward heights were measured by the rising platemeter. Herbage mass was harvested to determine reference herbage availability. The adequacy of estimating herbage mass was assessed using root mean squared error (RMSE) and mean bias. As the adequacy of the original equation was low, a new equation was developed using multiple regression models. The mean bias and the RMSE for the new equation were overall low with 201 kg dry matter/ha and 34.6%, but it tended to overestimate herbage availability at herbage mass < 500 kg dry matter/ha and underestimate it at >2500 kg dry matter/ha. Still, the newly developed equation for the microsonic sensor-based rising platemeter allows for accurate and precise estimation of available herbage mass on pastures.
Development of a sensor-based harrowing system using digital image analysis to achieve a uniform weed control selectivity in cereals
(2021) Spaeth, Michael; Gerhards, Roland
Using intelligent sensor technology for site-specific weed control can increase the efficacy of traditional weed control implements. Several scientific studies successfully used intelligent sensors for automatic harrow control by taking many different parameters into account such as weed density, soil resistance factor, and plant growth. However, none of the systems was practically feasible because these factors made the control system too complex and unattractive for farmers. Defining only one parameter (crop soil cover) instead of many provides a new and simple approach which was investigated in this work. The first scientific publication focuses on the development, practical implementation and testing of the automatic harrow control system. Two RGB-cameras were mounted before and after the harrow and constantly monitored crop cover. The CSC was then computed out of these resulting images. The image analysis, decision support system and automatic control of harrowing intensity by hydraulic adjustment of the tine angle were installed on a controller which was mounted on the harrow. Eight field experiments were carried out in spring cereals. Mode of harrowing intensity was changed in four experiments by speed, number of passes and tine angle. Each mode was varied in five intensities. In four experiments, only the intensity of harrowing was changed. Modes of intensity were not significantly different among each other. However, intensity had significant effects on WCE and CSC. Cereal plants recovered well from 10% CSC, and selectivity was in the constant range at 10% CSC. Therefore, 10% CSC was the threshold for the decision algorithm. If the actual CSC was below 10% CSC, intensity was increased. If the actual CSC was higher than 10%, intensity was decreased. The new system was tested in an additional field study. Threshold values for CSC were set at 10%, 30% and 60%. Automatic tine angle adjustment precisely realised the three different CSC values with variations of 1.5% to 3%. The next publication discussed and assessed the site-specific field adaptation of the development in cereals. In 2020, three field experiments were conducted in winter wheat and spring oats to investigate the response of the weed control efficacy and the crop to different harrowing intensities, in southwest Germany. In all experiments, six levels of CSC were tested. Each experiment contained an untreated control and an herbicide treatment as a comparison to the harrowing treatments. The results showed an increase in the WCE with an increasing CSC threshold. Difficult-to-control weed species such as Cirsium arvense (L.) and Galium aparine (L.) were best controlled with a CSC threshold of 70%. With a CSC threshold of 20% it was possible to control up to 98% of Thlaspi arvense (L.) The highest crop biomass, grain yield, and selectivity were achieved with an CSC threshold of 20–25% at all trial locations. With this harrowing intensity, grain yields were higher than in the herbicide control plots and a WCE of 68–98% was achieved. The last scientific article compares pairwise a conventional harrow intensity with automatic sensor-based harrowing intensity. Five field experiments in cereals were conducted at three locations in southwestern Germany in 2019 and 2020 to investigate if camera-based harrowing resulted in a more homogenous CSC and higher WCE, biomass, and crop grain yield than a conventional harrow with a constant intensity across the whole plot. For this purpose, pairwise comparisons of three fixed harrowing intensities (10 °, 40 °, and 70 ° tine angle) and three predefined CSC thresholds (CSC of 10%, 20%, and 60%) were realized in randomized complete block designs. Camera-based adjustment of the intensity resulted in 6-16% less standard deviation variation of CSC compared to fixed settings of tine angle. Crop density, WCE, crop biomass and grain yield were significantly higher for camera-based harrowing than for conventional harrowing. WCE and yields of all automatic adjusted harrowing treatments were equal to the herbicide control plots. In this PhD-thesis, a sensor-based harrow was developed and successfully investigated as an alternative to conventional herbicide application in cereals. A permanent, equal replacement of chemical weed control in arable farming systems can only be achieved using modern, sensor-based mechanical weed control approaches. Therefore, the efficacy of the mechanical weed control method can be improved and increased continuously. It has been shown that the precise adjustment of mechanical weed control methods to site-specific weed conditions allows similar WCE results as an herbicide application without causing yield losses. These findings contribute towards modern plant protection strategies to reduce the herbicide use and to establish the acceptance of technical progress in society.
Use of sensor technologies to estimate and assess the effect of various plant diseases on crop growth and development
(2008) Gröll, Kerstin; Claupein, Wilhelm
The topic of this study was ?Use of sensor technologies to estimate and assess the effect of various plant diseases on crop growth and development?. The background of the investigation can be seen in the challenge of developing a sensor system for the site-specific identification of plant diseases. The most widely used practice in disease control is still to spray fungicides uniformly over fields at different times during the vegetation period. However, most diseases are not distributed uniformly across a field, but occur in patches. During the early stage of epidemics large areas of the field are disease free. Excessive use of fungicides increases costs and can increase fungicides residue levels on agricultural products. As there is an increasing pressure to reduce their use by targeting fungicide spraying only on those places in the field where they are needed, the challenge is to provide farmers the the appropriate technological solutions. A simple and cost-effective optical device, based on the measurement of canopy reflectance in several wavebands, would allow disease patches to be identified and thus controlled. The implementation of these reflectance measurement data into crop growth models would allow for the development of site-specific decision rules whether to spray or not to spray. The specific objectives of the Ph.D. thesis were to: develop and test reflectance measurements as a possible technology to identify reflectance signatures of various plant diseases; develop suitable sets of calibrations that can be used for the identification and quantification of plant diseases; test different sensor systems at different spatial resolutions for their ability to identify plant diseases; develop a strategy to use plant disease information gained from sensor measurements as input dataset for the simulation of wheat growth under disease pressure in CERES-Wheat. In greenhouse experiments at the University of Hohenheim and in field experiments at the experimental station ?Ihinger Hof? of the University of Hohenheim the influence of the diseases powdery mildew, septoria leaf blotch and wheat eyespot on the reflectance of winter wheat was analyzed. To measure the reflectance of the plants three different sensor systems were used. Plant reflectance was measured with a digital camera (LEICA S1 PRO, LEICA Kamera AG, Solms, Germany) at leaf scale (0.5 cm²) and with the spectroradiometer Field Spec® Hand Held (ASD, Inc. Boulder, CO, USA) (0.5 m²) and the Yara N-Sensor in the field-scan modus (12 m²) 2 m above the canopy. The diseases powdery mildew, septoria leaf blotch and wheat eyespot have been analyzed. In a first approach it was tested if it is possible to detect plant diseases using reflectance measurements. The greenhouse studies showed that powdery mildew could be identified especially in the visible wavelength range. Also a correlation between powdery mildew pustules and reflectance changes was possible. Powdery mildew is a leaf disease and changes could directly be detected by a sensor system (Chapter 5). Out of this the second approach was to analyze if a stem disease that cannot directly be detected could be identified using a sensor system. The influence of wheat eyespot was investigated in a field experiment with winter wheat. The results showed that wheat eyespot could not be detected with the digital camera and the spectroradiometer. The problem was the low infection level and the distance between the measuring place and the infection place (Chapter 6). In a next step common vegetation indices were tested for their ability to identify plant diseases. Different vegetation indices were selected out of the literature to detect powdery mildew and septoria leaf blotch in the field using a spectroradiometer. Results indicated that the common vegetation index REIP was able to detect powdery mildew at an infection level of 7 %. With the common vegetation indices septoria leaf blotch could be detected only at a late infection level of 13.7 %. Out of this the new vegetation index DII was developed, which was able to detect septoria leaf blotch at an early infection level of 4 % (Chapter 7). Not only the place of infection but also the spatial resolution seems to play an important role in the identification of plant diseases. In a further approach different sensor systems with different spatial resolutions were tested in a field experiment for the identification of septoria leaf blotch. The results showed in general that septoria leaf blotch could be identified especially in the infrared wavelength range compared to powdery mildew that could especially identified in the visible wavelength range. The results showed further that the lower the spatial resolution , the more difficult it gets to identify plant diseases site-specifically. With a spatial resolution of 0.5 cm² a identification and quantification was possible. With a spatial resolution of 0.5 m² only a identification was possible and with a spatial resolution of 12 m² not identification and quantification was possible. That might be because of the resulting mixture of healthy and diseased plants (Chapter 8). The last step of this work was then to show how reflectance measurements could be implemented into crop growth models to calculate decisions whether to spray or not to spray fungicides on a site-specific level. Summarizing, the overall results of this study indicated that an identification of plant diseases was possible under certain conditions. An identification was possible if the infection place was also the measuring place and if a sensor system was used with a high spatial resolution. The results also showed that it was possible in a certain way to differ between biotroph and necrotroph plant diseases. For a holistic farming concept it is necessary in the future that reflectance measurements are integrated in a crop growth model to give farmers a decision tool that decides whether the infection is critical enough to spray or not.