Control system response for seed placement accuracy on row crop planters

Doctor of PhilosophyDepartment of Biological & Agricultural EngineeringAjay ShardaPlanting is one of the most critical field operations that can highly influence early season vigor, final plant density and ultimately potential crop yield. It is the opportunity to place seeds at a uniform depth and spacing providing them the ideal environment for proper growth and development. However, inherent field spatial variability could influence seed placement and requires proper implementation of planter settings to prevent shallow seeding depth, sidewall compaction and uneven spacing. The overall goal of this research is to evaluate the response of the planter and crop to downforce control system implementation across a wide range of machine and field operating conditions. Planting operations were performed in corn production fields using a Horsch row-crop planter with 12 row units equipped with a hydraulic downforce system capable of implementing fixed and active downforce settings. A custom-made data acquisition system was developed to record sensor data at 10 Hz sampling frequency. From this study, the following conclusions were drawn. First, soil texture and soil compaction due to tractor tires influenced real-time gauge wheel load (GWL). Implementing a fixed downforce setting with target GWL set at 35 kg showed that 25% of the total planting time GWL was less than 0 suggesting areas planted with uncertain seeding depth due to potential loss of ground contact of the gauge wheels. Likewise, fewer row units per section could provide lower variability in GWL indicating the need for an automatic section control to maintain target GWL within an acceptable range for all row units. Second, implementing an active downforce setting showed no significant difference between downforce A (63 kg) and downforce B (100 kg) on plant spacing, although downforce setting B resulted to higher plant spacing accuracy. Higher variability in spacing was observed when ground speed is over 12 kph. To achieve desired seeding depth, downforce greater than 100 kg is needed when ground speed is over 7.2 kph on no-till field and when ground speed is over 12 kph on strip-tilled field. Third, response of row units segregated in sections revealed that row unit acceleration on wing, track and non-track sections increases with speed. Strip-tilled soil exhibited lower row unit acceleration by 18% compared to no-till soil. Finally, a proof-of-concept sensing and measurement (SAM) system was developed to calculate seed spacing, depth and geo-location of corn. This system could provide real-time feedback on seed spacing and depth allowing appropriate downforce control system management for more consistent seed placement during planting. In summary, advances in planter technology paved the way for the addition of more row units across on the planter to increase planting productivity. With increasing width of planter toolbar, each row unit may need different downforce control to varying field and machine operating conditions. Appropriate downforce control management should be implemented to compensate for increased dynamics of planter row units across a highly variable field conditions to achieve the desired seed placement accuracy

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Precision Agriculture for Crop and Livestock Farming—Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.info:eu-repo/semantics/publishedVersio

WIRELESS SENSOR NETWORK EFFECTIVELY CONTROLS CENTER PIVOT IRRIGATION OF SORGHUM

Automatic irrigation scheduling has been demonstrated using wired sensors and sensor network systems with subsurface drip and moving irrigation systems. However, there are limited studies that report on crop yield and water use efficiency resulting from the use of wireless networks to automatically schedule and control irrigations. In this 2011 study, a multinode wireless sensor network (WSN) system was mounted onto a six-span center pivot equipped with a commercial variable rate irrigation (VRI) system. Data from the WSN was used to calculate an integrated crop water stress index (iCWSI) threshold for automatic irrigation scheduling of grain sorghum. Crop response to the automatic method was compared with manual irrigation scheduling using weekly direct soil water measurements. The WSN system was operational throughout 98% of the growing season, and the delivery rates for data packets from the different nodes ranged between 90% and 98%. Dry grain yields and WUE in the automatic and manual treatment plots were not significantly different from each other at any of the irrigation levels. Crop water use and WUE were highest in the I80% irrigation treatment level. Average seasonal integrated crop water stress indices were negatively correlated to irrigation treatment amounts in both the manual and automatic plots and correlated well to crop water use. These results demonstrate that it is feasible to use WSN systems for irrigation management on a field-scale level

A partial study of vertical distribution of conventional no-till seeders and spatial variability of seed depth placement of maize in the Alentejo region, Portugal

The requirements for a good stand in a no-till field are the same as those for conventional planting as well as added field and machinery management. Among the various factors that contribute towards producing a successful maize crop, seed depth placement is a key determinant. Although most no-till planters on the market work well under good soil and residue conditions, adjustments and even modifications are frequently needed when working with compacted or wet soils or with heavy residues. The main objective of this study, carried out in 2010, 2011 and 2012, was to evaluate the vertical distribution and spatial variability of seed depth placement in a maize crop under no-till conditions, using precision farming technologies and conventional no-till seeders. The results obtained indicate that the seed depth placement was affected by soil moisture content and forward speed. The seed depth placement was negatively correlated with soil resistance and seeding depth had a significant impact on mean emergence time and the percentage of emerged plants. Shallow average depth values and high coefficients of variation suggest a need for improvements in controlling the seeders’ sowing depth mechanism or more accurate calibration by operators in the field

Automatic expert system based on images for accuracy crop row detection in maize fields

This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient

Development of an Autonomous Aerial Toolset for Agricultural Applications

According to the United Nations, the world population is expected to grow from its current 7 billion to 9.7 billion by the year 2050. During this time, global food demand is also expected to increase by between 59% and 98% due to the population increase, accompanied by an increasing demand for protein due to a rising standard of living throughout developing countries. [1] Meeting this increase in required food production using present agricultural practices would necessitate a similar increase in farmland; a resource which does not exist in abundance. Therefore, in order to meet growing food demands, new methods will need to be developed to increase the efficiency of farming, thereby increasing yield from the present land. One way in which this problem can be solved is through the usage of autonomous aerial systems to scout for problems which could potentially affect the crop yield – such as nutrient deficiency, water stress, or diseases. Once located, this data can be used to determine the proper treatment for the field to alleviate the problem. Through this process, resources can be reduced to the required minimum, while problems affecting the crop yield will still be corrected, allowing greater production with a lower amount of resources. This project on the application of Unmanned Aerial Vehicles (UAV’s) to the field of agriculture consisted of two phases. First, a study was conducted on the required background to define the problem statement and what solutions were available for this application. This consisted of first defining the operations within agriculture where UAV’s could be used to increase efficiency, and then the sensors, hardware, and software these operations would require. The remainder of the project consisted of evaluating the tools which could be utilized to develop such a solution. Primarily, the project focused on software tools – programming software, simulation environments, and machine learning algorithms – which could be utilized by future students to develop a functional hardware and software toolchain for the research of autonomous systems for agricultural applications. After analyzing these development solutions, a set of tools was selected which showed promise in the creation of a functional solution. It was demonstrated that the core functions required for a UAV-based agricultural solution – navigation, perception, and feature detection – could be implemented within these systems, implying that they could be integrated into a full solution. As the tools were selected to ensure the developed algorithms would be transferable to physical platforms, this additionally supports a physical system could also be developed. The present work is part of the Autonomous Systems Lab which belongs to the WKU Center for Energy Systems. The author hopes that this project contributes to the advancement of the curriculum within the engineering department and serves as a foundation for future students developing autonomous systems, perception, and applied artificial intelligence at WKU

A study of integrated weed control strategies for establishing soybean (Glycine max L. MERR.) in the German production system

Soybean (Glycine max L. MERR.) has expanded to become one of the most traded agriculture products worldwide in recent decades. Europe is one of the primary importing regions; however, the dependence on soybean imports has been critically assessed by the public. To reduce the dependency on soybean imports, increased local soybean production should be favoured. In addition to environmental conditions, weeds are a major limiting factor for soybean yield under German climate conditions. Weeds can be successfully controlled with herbicides, although crop injury frequently occurs after application. Sensor-based screening would be helpful for a rapid evaluation of cultivar tolerance to herbicide application. Alternatively, mechanical weed control strategies can be applied. Since soybean production is currently introduced to the regional crop production, weed control efficiency of conventional mechanical tools (e.g., hoeing and harrowing) have to be evaluated. By using automatic guiding systems intra-row elements could be utilised to increase the weed control efficiency of mechanical hoeing. Other than that, agronomical practices such as the tillage system or cover crops influences the occurrence of weeds. The most common practise worldwide for soybean cultivation is the no-tillage system, which has not yet been investigated under local conditions. Therefore, different weed control strategies in soybean production were investigated according to the following major objectives of this thesis: - Detection of crop injury by herbicides using a chlorophyll fluorescence imaging sensor for different soybean cultivars. - Evaluation of the conventional mechanical strategies of hoeing and harrowing in soybean. - Examination of the weed control efficiency in inter- and intra-row areas using RTK-GNSS precision steering and an optical camera guiding system for mechanical weed control in soybean. - Evaluation of the efficiency of tillage, reduced tillage and no- tillage cultivation systems and the influence of cover crops on weed suppression in local soybean production. The Imaging-PAM-sensor based on chlorophyll fluorescence imaging was utilised to investigate the response of different soybean cultivars to the application of herbicides. The measurements indicated significant differences with respect to injury to the cultivars after herbicide application. Herbicides containing the active ingredient metribuzin resulted in significant differences in the level of crop injury depending on the cultivar. The active ingredients dimethenamid and clomazone resulted in less injury, independent of the cultivar. The PAM-sensor was able to detect stress symptoms 3 to 7 days before visual symptoms appeared. An investigation of hoeing and harrowing, which are conventional mechanical techniques for weed control, showed 78% and 72% weed control efficiency, respectively. In further experiments, the results of precision steering systems using RTK-GNSS and an optical camera guiding system additionally equipped with intra-row elements (e.g., finger weeders) were compared with the results of conventional hoeing. Mechanical weed control using automatic steering technology and an intra-row element (finger weeder) reduced the weed density by 89% compared with 68% in the conventional hoeing system. With respect to crop yields, statistical benefits of precision steering were not observed. However, the driving speed could be increased from 4 km h−1 in the conventional hoeing system to 10 km h−1 using the automatic steering systems. In an additional experiment, two cover crops species, rye (Secale cereale L.) and barley (Hordeum vulgare L.), were grown for preventive weed control in soybean production. The cover crops were transformed into a mulch layer using a roller-crimper immediately before soybean was sown using a no-tillage technique. Conventional tillage was performed to compare the systems with respect to their weed control efficiency, crop development and soybean yield. The results showed that the no-tillage system had a greater effect on suppressing summer annual weed species (Chenopodium album (L.), Echinochloa crus-galli (L.) P. Beauv. and Amaranthus retroflexus (L.)) than the tillage systems. Conventional tillage and reduced tillage showed increased suppression of the weed species Matricaria inodora (L.), Stellaria media (L.) Vill. and Sonchus arvensis (L.), which were present in the no-tillage system. However, in the conventional tillage and reduced tillage systems, an additional weed control treatment was necessary to suppress the summer annual weeds and ensure high yields. The cover crop rye resulted in weed control similar to that of barley in the no-tillage system. Despite the low weed density, the no-tillage system with a rolled cover crop showed a yield reduced of 47%, whereas the yield of the reduced tillage system was decrease by 23% compared with the conventional tillage system.In den letzten Jahrzehnten entwickelte sich die Sojabohne (Glycine max L. MERR.) zu einem der meist gehandelten Agrarprodukte der Welt. In Europa besteht ein großer Bedarf an Sojaprodukten, welcher hauptsächlich über Import gedeckt wird. Die Abhängigkeit von Importware und die damit verbundenen sozioökologischen und -ökonomischen Auswirkungen des Sojaanbaus in den Exportländern werden in der Öffentlichkeit kritisch gesehen. Eine Ausdehnung des heimischen Sojaanbaus kann der Abhängigkeit von Importware entgegenwirken. Neben der Sortenanpassung an die klimatischen Bedingungen in Europa stellt die Unkrautkontrolle eine weitere große Herausforderung hierfür dar. Derzeit können die Unkräuter mit Hilfe der zugelassenen Herbizide erfolgreich bekämpft werden, wobei dies oftmals zu Schäden an der Kulturpflanze führt. Die zugelassenen Sojasorten zeigen unterschiedliche Kulturverträglichkeiten gegenüber den Herbiziden. Die Unkräuter können zudem mechanisch oder durch den Einsatz von Zwischenfruchtmulch unterdrückt werden Basierend auf diesen Möglichkeiten zur Unkrautkontrolle leiten sich folgende Zielsetzungen der Arbeit ab: - Erkennung von Herbizidstress an verschiedenen Sojasorten mit Hilfe eines Chlorophyllfluoreszenz-Sensors. - Bewertung von Hack- und Striegelmaßnahmen zur mechanischen Unkrautbekämpfung im Sojaanbau. - Beurteilung der Effizienz automatischer Lenksysteme (GNSS-RTK und eine Kamera geführte Hacke mit Verschieberahmen) Unkräuter zwischen und in der Reihe zu bekämpfen. - Bewertung von wendender und reduzierter Bodenbearbeitung sowie Direktsaat und den Einfluss von Zwischenfrüchten auf das Unkrautaufkommen im Sojaanbau. Zur Bewertung des Einflusses von Herbiziden auf die Pflanzengesundheit wurde mit dem Imaging-PAM-Sensor die Chlorophyllfluoreszenz verschiedener Sojasorten nach der Applikation untersucht. Die Ergebnisse zeigten signifikante Unterschiede nach der Messung mit dem Imaging-PAM-Sensor in Bezug auf den Herbizidstress der Sojapflanze. Nach der Herbizidapplikation mit dem Wirkstoff Metribuzin wiesen einige Sorten signifikante Unterschiede der Chlorophyllfluoreszenz im Vergleich zur Kontrolle auf. Die Wirkstoffe Dimethenamid und Clomazone führten bei allen Sorten zu einer geringeren Stressreaktion. Der Sensor konnte den Herbizidstress an den Kulturpflanzen 3 bis 7 Tage vor dem Auftreten von visuellen Symptomen messen. In den Versuchen zur mechanischen Unkrautkontrolle ergab die klassische Behandlung mit der Reihen-Hacke einen Bekämpfungserfolg von 78% und mit dem Striegel eine Reduktion der Unkräuter von 72% verglichen zur unbehandelten Kontrolle. In einem weiteren Experiment wurden die Precision Farming Technologien GNSS-RTK und eine Kamera gesteuerte Hacke mit Verschieberahmen eingesetzt. Die Systeme wurden mit zusätzlichen Werkzeugen (z.B. Fingerhacke) für die Unkrautkontrolle in der Reihe ausgestattet und die Ergebnisse mit einer konventionellen Hacke verglichen. Hierbei ergab sich für das automatisch geführte System mit Fingerhacke ein Unkrautbekämpfungserfolg von 89%, während konventionelles Hacken 68% Bekämpfungserfolg erzielte. In Bezug auf den Ertragszuwachs ergab sich keine signifikante Erhöhung durch den Einsatz von automatischen Lenksystemen, jedoch konnte dadurch die Fahrgeschwindigkeit von 4 km h-1 auf 10 km h-1 erhöht werden. In weiteren Versuchen im Rahmen dieser Arbeit wurden Roggen (Secale cereale L.) und Gerste (Hordeum vulgare L.) als Zwischenfrüchte zur vorbeugenden Unkrautkontrolle vor Soja angebaut. Unmittelbar vor der Aussaat der Sojabohnen mit einer Direktsaatmaschine wurden die Zwischenfrüchte mit einer Messerwalze niedergedrückt. In einer weiteren Variante wurden die Zwischenfrüchte von der Fläche abgefahren und die Stoppeln daraufhin flach bearbeitet. Um die Ergebnisse dieser beiden Systeme bezüglich Unkrautkontrolle, Kulturpflanzenentwicklung und Ertrag einordnen zu können, wurde eine Variante mit klassischer wendender Bodenbearbeitung (Pflug) angelegt. Die Ergebnisse zeigten eine deutlich gesteigerte Unterdrückung der Unkrautarten Chenopodium album (L.), Echinochloa crus-galli (L.) P. Beauv. und Amaranthus retroflexus (L.) in dem Direktsaatsystem verglichen mit den Varianten mit reduzierter oder wendender Bodenbearbeitung. Jedoch wies die Direktsaatvariante einen signifikanten Anstieg der Arten Matricaria inodora (L.), Stellaria media (L.) Vill.) und Sonchus arvensis (L.) auf. In den Varianten mit reduzierter oder wendender Bodenbearbeitung war eine zusätzliche Bekämpfung der vorwiegend sommerannuellen Unkräuter während der Vegetation notwendig, um die Erträge zu sichern. Im Direktsaatsystem zeigten die beiden eingesetzten Zwischenfrüchte Roggen und Gerste keine Unterschiede in ihrer unkrautunterdrückenden Wirkung. Trotz der teilweise höheren Unterdrückung der Unkräuter wies das Direktsaatsystem eine Ertragsreduktion von 47% auf. Die Variante mit reduzierter Bodenbearbeitung ergab einen Ertragsrückgang von 23% verglichen zum System mit wendender Bodenbearbeitung

Research status of agricultural robot technology

According to the different agricultural production uses, agricultural robots were classified, mainly including agricultural information collection robots, pruning robots, grafting robots, transplanting robots, spraying robots and picking robots. The research status of mainstream agricultural robots at home and abroad were introduced, and their working principles and characteristics were expounded. Finally, the problems existing in the key technologies of existing agricultural robots and their future development directions were put forward