A current PhD student in Molecular Plant Genetics who has a masters in Agrobiotechnology.
Meeting the growing food demand in a sustainable way - there is light at the end of the tunnel
Global population is set to climb to 9.6 billion by 2050 and it is a serious challenge for scientists to innovate sustainable methodologies, which will enable the world to grow enough food for these extra hungry people. Inevitable urbanization and rapidly changing pattern of climate are crucial aspects that scientists need to keep in mind while developing such strategies. Even though the situation seems dire, there is hope due to groundbreaking advents in the field of plant molecular genetics, engineering, computer science and information technology in the later part of 20th century. Scientists from all these branches have come together to introduce an integrated field widely known as “precision agriculture (PA) or precision farming" which is dramatically transforming food production process that we have seen in the past century.
The main motto of precision PA is “more with less”. As cultivable lands are decreasing, so scientists are focusing on making best use of every square meter of available farmland by delivering higher yield. PA could be divided into 4 main stages such as precision soil preparation (tilling), precision seeding, precision crop management and precision harvesting. Each of these steps is regulated by state-of-the-art technologies as 70-80% of the new farming equipments sold now-a-days have built in precision farming component. Farmers can now cost-effectively prepare loosened soil by using machines that eliminate weeds and blend organic matters. Besides, they can determine soil quality by geomapping (soil density, moisture, quality etc.) and then use machines to sow seeds at correct depth and distance, which is important in order to ensure enhanced productivity. Usage of crop sensors and remote sensing, drones, variable rate technology (VRT), satellite steering systems (GPS) for applying optimum fertilizer, pesticide, insecticide and water have become a common practice that assists farmers to minimize cost and environmental pollution while safeguarding uniform growth in the field. Availability of combine harvesters which finishes 3 steps of harvesting process namely reaping, threshing and winnowing in one step with high speed, accuracy and less time have really made life easier for farmers, ultimately making agro-business a highly profitable one.
To conclude, the introduction of PA into practice over the last two decades could definitely be considered as a giant leap for mankind and it is set to develop more remarkably in future.
Abstract: Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Adapting production inputs site-specifically within a field and individually for each animal allows better use of resources to maintain the quality of the environment while improving the sustainability of the food supply. Precision agriculture provides a means to monitor the food production chain and manage both the quantity and quality of agricultural produce.
Pub.: 13 Feb '10, Pinned: 22 Apr '17
Abstract: Precision Agriculture (PA) can help in managing crop production inputs in an environmentally friendly way. By using site-specific knowledge, PA can target rates of fertilizer, seed and chemicals for soil and other conditions. PA substitutes information and knowledge for physical inputs. A literature review indicates PA can contribute in many ways to long-term sustainability of production agriculture, confirming the intuitive idea that PA should reduce environmental loading by applying fertilizers and pesticides only where they are needed, and when they are needed. Precision agriculture benefits to the environment come from more targeted use of inputs that reduce losses from excess applications and from reduction of losses due to nutrient imbalances, weed escapes, insect damage, etc. Other benefits include a reduction in pesticide resistance development. One limitation of the papers reviewed is that only a few actually measured directly environmental indices, such as leaching with the use of soil sensors. Most of them estimated indirectly the environmental benefits by measuring the reduced chemical loading. Results from an on-farm trial in Argentina provide an example of how site-specific information and variable rate application could be used in maintaining profitability while reducing N applications. Results of the sensitivity analysis show that PA is a modestly more profitable alternative than whole field management, for a wide range of restrictions on N application levels. These restrictions might be government regulations or the landowner's understanding of environmental stewardship. In the example, variable rate of N maintains farm profitability even when nitrogen is restricted to less than half of the recommended uniform rate.
Pub.: 01 Aug '04, Pinned: 22 Apr '17
Abstract: Precision agriculture was initiated in the mid 1980s, using newly available technologies, to improve the application of fertilizers by varying rates and blends as needed within fields. Presently, the concept has been adapted to a variety of practices, crops, and countries. Its adoption varies significantly by cropping system, regions, and countries but it is progressively introduced or evaluated around the world. Several types of challenges limit a broader adoption: socio-economical, agronomical, and technological. Socio-economical barriers are principally costs and lack of skills. Agronomical challenges are lack of basic information, inadequate sampling and scouting procedures, absence of site-specific fertilizer recommendations, misuse of information, and lack of qualified agronomic services. There are multiple technological barriers that relate to machinery, sensor, GPS, software, and remote sensing. However, these barriers will be progressively lifted and precision agriculture will be a significant component of the agricultural system of the future. It offers a variety of potential benefits in profitability, productivity, sustainability, crop quality, food safety, environmental protection, on-farm quality of life, and rural economic development.
Pub.: 01 Nov '02, Pinned: 23 Apr '17
Abstract: To maximize economic return from agricultural production units, costs have to be minimized and benefits maximized. For grain, kernel yield and quality have to be maximized while the use of seeds, fertilizer, herbicides and fungicides have to be optimized.The best location to evaluate productivity levels, by measuring yield and quality of grain and straw, is the combine harvester. Moreover, other grain quality characteristics like density or test weight can be determined for use as an evaluation tool. In this paper, an overview is given of the past and current research toward the evaluation of currently available commercial sensors (e.g., for measuring grain yield and grain moisture content) as well as toward the development of new sensors (e.g., grain protein content and straw yield).
Pub.: 01 Jun '02, Pinned: 23 Apr '17
Abstract: High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution required rather than the geo-referencing accuracy. Regarding the crop mis-alignment, the results showed that the overall errors were less than twice the spatial resolution, which did not break the crop line continuity at the studied spatial resolutions (pixels from 7.4 to 24.7 mm for 30, 60 and 100 m flying altitudes respectively) on the studied crop (early wheat). The results lead to the conclusion that a UAV flying at a range of 30 to 100 m altitude and using a moderate number of GCPs is able to generate ultra-high spatial resolution ortho-imagesortho-images with the geo-referencing accuracy required to map small weeds in wheat at a very early phenological stage. This is an ambitious agronomic objective that is being studied in a wide research program whose global aim is to create broad-leaved and grass weed maps in wheat crops for an effective ESSWM.
Pub.: 08 Nov '13, Pinned: 23 Apr '17
Abstract: Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology.
Pub.: 18 Jul '15, Pinned: 23 Apr '17
Abstract: Weed infestations and associated yield losses require effective weed control measures in soybean and sugar beet. Besides chemical weed control, mechanical weeding plays an important role in integrated weed management systems. Field experiments were conducted at three locations for soybean in 2013 and 2014 and at four locations for sugar beet in 2014 to investigate if automatic steering technologies for inter-row weed hoeing using a camera or RTK-GNSS increase weed control efficacy, efficiency and crop yield. Treatments using precision farming technologies were compared with conventional weed control strategies. Weed densities in the experiments ranged from 15 to 154 plants m−2 with Chenopodium album, Polygonum convolvulus, Polygonum aviculare, Matricaria chamomilla and Lamium purpureum being the most abundant species. Weed hoeing using automatic steering technologies reduced weed densities in soybean by 89% and in sugar beet by 87% compared to 85% weed control efficacy in soybean and sugar beet with conventional weeding systems. Speed of weed hoeing could be increased from 4 km h−1 with conventional hoes to 7 and 10 km·h−1, when automatic steering systems were used. Precision hoeing technologies increased soybean yield by 23% and sugar beet yield by 37%. After conventional hoeing and harrowing, soybean yields were increased by 28% and sugar beet yield by 26%.
Pub.: 23 Apr '15, Pinned: 23 Apr '17
Abstract: Crop growth and yield monitoring over agricultural fields is an essential procedure for food security and agricultural economic return prediction. The advances in remote sensing have enhanced the process of monitoring the development of agricultural crops and estimating their yields. Therefore, remote sensing and GIS techniques were employed, in this study, to predict potato tuber crop yield on three 30 ha center pivot irrigated fields in an agricultural scheme located in the Eastern Region of Saudi Arabia. Landsat-8 and Sentinel-2 satellite images were acquired during the potato growth stages and two vegetation indices (the normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI)) were generated from the images. Vegetation index maps were developed and classified into zones based on vegetation health statements, where the stratified random sampling points were accordingly initiated. Potato yield samples were collected 2-3 days prior to the harvest time and were correlated to the adjacent NDVI and SAVI, where yield prediction algorithms were developed and used to generate prediction yield maps. Results of the study revealed that the difference between predicted yield values and actual ones (prediction error) ranged between 7.9 and 13.5% for Landsat-8 images and between 3.8 and 10.2% for Sentinel-2 images. The relationship between actual and predicted yield values produced R2 values ranging between 0.39 and 0.65 for Landsat-8 images and between 0.47 and 0.65 for Sentinel-2 images. Results of this study revealed a considerable variation in field productivity across the three fields, where high-yield areas produced an average yield of above 40 t ha-1; while, the low-yield areas produced, on the average, less than 21 t ha-1. Identifying such great variation in field productivity will assist farmers and decision makers in managing their practices.
Pub.: 10 Sep '16, Pinned: 23 Apr '17
Abstract: Modern communication, sensing, and actuator technologies as well as methods from signal processing, pattern recognition, and data mining are increasingly applied in agriculture. Developments such as increased mobility, wireless networks, new environmental sensors, robots, and the computational cloud put the vision of a sustainable agriculture for anybody, anytime, and anywhere within reach. Yet, precision farming is a fundamentally new domain for computational intelligence and constitutes a truly interdisciplinary venture. Accordingly, researchers and experts of complementary skills have to cooperate in order to develop models and tools for data intensive discovery that allow for operation through users that are not necessarily trained computer scientists. We present approaches and applications that address these challenges and underline the potential of data mining and pattern recognition in agriculture.
Pub.: 07 Aug '13, Pinned: 22 Apr '17
Abstract: The USDA-Agricultural Research Service and Colorado State University are conducting an interdisciplinary study that focuses on developing a clearer scientific understanding of the causes of yield variability. Two years of data have been collected from two commercial center pivot irrigated fields (72 and 52 ha). Cooperating farmers manage all farming operations for crop production and provide yield maps of the maize grown on the fields. The farmers apply sufficient inputs to minimize risk of yield loss. The important variables for crop production have been sampled at a grid spacing of 76 m for two seasons. A spatial auto-regressive model was fitted to the data to determine the critical factors affecting yield variability. Thirty one layers of data were included in the analysis, and a total of over 140,000 models were examined. Up to five predictors were used in each model. Variability in water application, nitrate nitrogen, organic matter, phosphorus, topology, percent silt and soil electrical conductivity were significant in explaining the yield variability for Field 1. Variability in water application, ammonium, nematodes, percent clay, insects, potassium, soil electrical conductivity, and topology were significant in explaining the yield variability for Field 2. The tentative conclusion is that the potential economic benefit of site specific management is small where the farmer's management tolerance for risk is low. The potential of site specific management is in reducing the cost of inputs and environmental impact, but could increase risk.
Pub.: 01 Mar '02, Pinned: 22 Apr '17
Abstract: The traditional role of field days and tours has been to introduce growers and agricultural professionals to new technologies and techniques so that the audience could see how these technologies or techniques could be practically used and applied. Based on this concept, the use of field days or tours to demonstrate the radically new technologies and site-specific management techniques behind precision farming is a perfect application of these tools. Indeed, a survey of precision farming field days held in a number of states found that field days were beneficial in showing growers and agricultural professionals global positioning systems, yield monitoring systems, techniques for grid soil sampling, software for geographic information systems, vehicle guidance systems, variable-rate application equipment, and a host of other technologies and processes. In particular, hands-on experiences, such as field demonstrations, guided sampling activities, and combine harvesting demonstrations are extremely well received and valuable. Indoor seminars featuring farmer panels, side-by-side software demonstrations, and demonstrations of geographic information systems have received high marks by participants. The survey found that a field day must be centered on a well-defined objective and a thorough understanding of the needs of the audience. Survey respondents unanimously agreed that precision farming field days and tours will be even more important as future advances in technology and management techniques are discovered. However, future precision agriculture field days or tours must be coupled with other issues or topics where precision agriculture technologies can be used to solve a practical problem and enhance management practices.
Pub.: 01 Dec '02, Pinned: 22 Apr '17
Abstract: Precision Agriculture, also known as Precision Farming, or Prescription Farming, is a modern agriculture technolgy system, which brings “precision” into agriculture system. All concepts of Precision Agriculture are established on the collection and management of variable cropland information. As the tool of collecting, managing and analyzing spatial data, GIS is the key technology of integrated Precision Agriculture system. This article puts forward the concept of Farmland GIS and designs Farmland GIS into five modules, and specifies the functions of the each module, which builds the foundation for practical development of the software. The study and development of Farmland GIS will propel the spreading of Precision Agriculture technology in China.
Pub.: 01 Mar '03, Pinned: 22 Apr '17
Abstract: Research on Precision Farming (PF) relates the adoption of PF primarily to economic incentives as well as farm attributes, whereas social factors are commonly ignored. Therefore, the present study analyses the importance of farmers’ communication and co-operation strategies in the adoption of PF and their relation to farm attributes. Forty-nine qualitative interviews with stakeholders from the agricultural sector were conducted. The survey was based in Germany where most interviews took place and reflected with findings from the Czech Republic, Denmark and Greece. It is revealed that farms differ in their communication strategies depending on farm size. Joint investment in PF was only reported from some regions. It can be assumed that agricultural contractors will be major driving forces behind the adoption of PF over the next 10 years, especially in areas with smaller-sized farms. Agricultural data processing by service providers is seen as a common issue. Concerns regarding potential data misuse, over-regulation and software compatibility were raised.
Pub.: 21 Nov '09, Pinned: 22 Apr '17
Abstract: A web-based decision support tool, zone mapping application for precision farming (ZoneMAP, http://zonemap.umac.org), has been developed to automatically determine the optimal number of management zones and delineate them using satellite imagery and field data provided by users. Application rates, such as of fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming equipment. ZoneMAP is linked to Digital Northern Great Plains, a web-based application which hosts an archive of satellite imagery, as well as high resolution imagery from airborne sensors. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well and were consistent with zone maps created by traditional means. The results demonstrated that ZoneMAP can serve as an effective and easy-to-use tool for those who practice precision agriculture.
Pub.: 12 Aug '09, Pinned: 22 Apr '17
Abstract: Real-time controlled pesticide application requires a fast reacting system, enabling continuous variation of pesticide type and concentration based on a control signal. Direct injection systems meet the requirements for such application; however there are significant differences in the response time and quality of single system designs. This article discusses the complex of problems in direct injection systems in connexion with real-time control and proposes optimisations of system parts in order to reduce the response time and improve the quality of the process. The system response time was reduced to 160 ms for 1% output concentration by optimization of the control process, injection assembly and mixing chamber design.
Pub.: 15 Nov '08, Pinned: 22 Apr '17