Crop Scouting Research Articles

AI Robots in Various Sector

The convergence of Artificial Intelligence (AI) and robotics is revolutionizing multiple sectors, enhancing productivity, safety, and quality. In manufacturing, AI-driven robots automate assembly lines, optimize logistics, and perform precision tasks. These robots adapt to changing production demands, improving efficiency and reducing errors. In agriculture, vision-based AI assists robots in weed detection, crop scouting, disease identification, and harvesting. By integrating AI algorithms with sensors, agricultural robots enhance yield prediction and resource management. In healthcare, surgical robots aid in minimally invasive procedures, while AI algorithms analyze medical images for early disease detection. The synergy between AI and robotics holds immense potential, reshaping industries and advancing human well-being

Customer Value Proposition for Precision Farming

A company's value proposition is established by the distinctive benefits and values it offers to its clients. Site-specific crop management (SSCM) and precision agriculture (PA) are farming management techniques that use technology to monitor and evaluate certain areas and crops. Between 2023 and 2030, the market for precision agriculture is anticipated to increase at a CAGR of 12.6%, reaching $15.6 billion. Precision farm planning, field maps, crop scouting, yield maps, and the estimation of input quantities are driving factors in this growth, along with the Internet of Things and advanced analytics. A company's commitment to influencing the attitudes, beliefs, and behavior of its target market is stated in its value proposition. The goal of the study was to address agricultural issues and increase productivity and profitability for banana farmers in the Anand and Umreth Talukas. Educate stakeholders, decision-makers, and service providers on how to support precision farming techniques. The study, conducted in Anand and Umreth Taluka, Gujarat, involved 50 banana growers with over 2 ha of land, using a descriptive design and semi-structured survey. Formulating a customer value proposition for identifying the problems faced by banana growers will help in understanding the current issues and areas of improvement within the agricultural sector. The findings of this research were contributing to the knowledge and understanding of precision farming in Anand and Umreth Talukas. Customers place a premium on product quality, the accessibility of the sales staff, and price, while farmers must deal with issues including disease, weather, nutrition, insect pests, and irrigation. Irrigation was viewed as less significant than weather and illness, which were found the major problems in the study area. Decision-making in agriculture depends heavily on weather forecasts and practices. A substantial effort was needed to improve agricultural practices because of the influence of climate change, input market accessibility, and remote farm access.

Design of a Reconfigurable Crop Scouting Vehicle for Row Crop Navigation: A Proof-of-Concept Study.

Pest infestation causes significant crop damage during crop production, which reduces the crop yield in terms of quality and quantity. Accurate, precise, and timely information on pest infestation is a crucial aspect of integrated pest management practices. The current manual scouting methods are time-consuming and laborious, particularly for large fields. Therefore, a fleet of scouting vehicles is proposed to monitor and collect crop information at the sub-canopy level. These vehicles would traverse large fields and collect real-time information on pest type, concentration, and infestation level. In addition to this, the developed vehicle platform would assist in collecting information on soil moisture, nutrient deficiency, and disease severity during crop growth stages. This study established a proof-of-concept of a crop scouting vehicle that can navigate through the row crops. A reconfigurable ground vehicle (RGV) was designed and fabricated. The developed prototype was tested in the laboratory and an actual field environment. Moreover, the concept of corn row detection was established by utilizing an array of low-cost ultrasonic sensors. The RGV was successful in navigating through the corn field. The RGV’s reconfigurable characteristic provides the ability to move anywhere in the field without damaging the crops. This research shows the promise of using reconfigurable robots for row crop navigation for crop scouting and monitoring which could be modular and scalable, and can be mass-produced in quick time. A fleet of these RGVs would empower the farmers to make meaningful and timely decisions for their cropping system.

AI-Assisted Vision for Agricultural Robots

Robotics has been increasingly relevant over the years. The ever-increasing demand for productivity, the reduction of tedious labor, and safety for the operator and the environment have brought robotics to the forefront of technological innovation. The same principle applies to agricultural robots, where such solutions can aid in making farming easier for the farmers, safer, and with greater margins for profit, while at the same time offering higher quality products with minimal environmental impact. This paper focuses on reviewing the existing state of the art for vision-based perception in agricultural robots across a variety of field operations; specifically: weed detection, crop scouting, phenotyping, disease detection, vision-based navigation, harvesting, and spraying. The review revealed a large interest in the uptake of vision-based solutions in agricultural robotics, with RGB cameras being the most popular sensor of choice. It also outlined that AI can achieve promising results and that there is not a single algorithm that outperforms all others; instead, different artificial intelligence techniques offer their unique advantages to address specific agronomic problems.

On-farm trials towards reduced insecticides in main crop potatoes in the Waikato Region of New Zealand

Tomato potato psyllid (TPP), Bactericera cockerelli (Sulc) (Hemiptera: Triozidae), is the vector for “Candidatus Liberibacter solanacearum” (CLso), which causes the serious disease “zebra chip” (ZC) of potatoes. Between 2016 and 2019, a reduced-insecticide approach to control TPP was evaluated. We compared a standard-insecticide weekly spray regime that included Integrated Pest Management (IPM)-compatible insecticides plus JMS Stylet Oil® (JMS) as a wetting agent, with a reduced-insecticide regime where we used the oil on its own on alternate weeks with the insecticide/oil mixtures. Spray programme start dates were determined by: (1) crop scouting; (2) sticky-trap monitoring; and (3) degree-day calculation. Crop scouting combined with a sticky-trap action threshold and degree-day data was an effective method for determining when to start spraying. The most substantial reduction in insecticides was achieved by alternating weekly insecticides with the oil formulation on its own. Sub-samples of TPP from sticky traps situated in the trials tested for CLso confirmed the presence of the bacteria in (commonly known as ‘hot’) TPP throughout the trials. The reduced-treatment approach gave statistically similar levels of ZC to the standard insecticide spray programme.

Discrimination between Eggs from Stink Bugs Species in Europe Using MALDI-TOF MS.

Simple SummaryRecent globalization of trade and travel has led to the introduction of exotic insects into Europe, including the brown marmorated stink bug (Halyomorpha halys)—a highly polyphagous pest with more than 200 host plants, including many agricultural crops. Unfortunately, farmers and crop-protection advisers finding egg masses of stink bugs during crop scouting frequently struggle to identify correctly the species based on their egg masses, and easily confuse eggs of the invasive H. halys with those of other (native) species. To this end, we have investigated using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) for rapid, fieldwork-compatible, and low-reagent-cost discrimination between the eggs of native and exotic stink bugs.In the current paper, we used a method based on stink bug egg-protein immobilization on filter paper by drying, followed by post-(storage and shipping) extraction in acidified acetonitrile containing matrix, to discriminate between nine different species using MALDI-TOF MS. We obtained 87 correct species-identifications in 87 blind tests using this method. With further processing of the unblinded data, the highest average Bruker score for each tested species was that of the cognate reference species, and the observed differences in average Bruker scores were generally large and the errors small except for Capocoris fuscispinus, Dolycoris baccarum, and Graphosoma italicum, where the average scores were lower and the errors higher relative to the remaining comparisons. While we observed clear discrimination between the nine species using this method, Halyomorpha halys and Piezodorus lituratus were more spectrally related than the other pairwise comparisons.

Validating the Strategic Deployment of Blackleg Resistance Gene Groups in Commercial Canola Fields on the Canadian Prairies.

Blackleg, caused by the fungal pathogen Leptosphaeria maculans, is a serious threat to canola (Brassica napus L.) production in western Canada. Crop scouting and extended crop rotation, along with the use of effective genetic resistance, have been key management practices available to mitigate the impact of the disease. In recent years, new pathogen races have reduced the effectiveness of some of the resistant cultivars deployed. Strategic deployment and rotation of major resistance (R) genes in cultivars have been used in France and Australia to help increase the longevity of blackleg resistance. Canada also introduced a grouping system in 2017 to identify blackleg R genes in canola cultivars. The main objective of this study was to examine and validate the concept of R gene deployment through monitoring the avirulence (Avr) profile of L. maculans population and disease levels in commercial canola fields within the Canadian prairies. Blackleg disease incidence and severity was collected from 146 cultivars from 53 sites across Manitoba, Saskatchewan, and Alberta in 2018 and 2019, and the results varied significantly between gene groups, which is likely influenced by the pathogen population. Isolates collected from spring and fall stubble residues were examined for the presence of Avr alleles AvrLm1, AvrLm2, AvrLm3, AvrLm4, AvrLm5, AvrLm6, AvrLm7, AvrLm9, AvrLm10, AvrLm11, AvrLepR1, AvrLepR2, AvrLep3, and AvrLmS using a set of differential host genotypes carrying known resistance genes or PCR-based markers. The Simpson’s evenness index was very low, due to two dominant L. maculans races (AvrLm2-4-5-6-7-10-11 and AvrLm2-5-6-7-10-11) representing 49% of the population, but diversity of the population was high from the 35 L. maculans races isolated in Manitoba. AvrLm6 and AvrLm11 were found in all 254 L. maculans isolates collected in Manitoba. Knowledge of the blackleg disease levels in relation to the R genes deployed, along with the L. maculans Avr profile, helps to measure the effectiveness of genetic resistance.

A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture

Presently in agriculture, there is much ample scope for drone and UAS (Unmanned Aircraft System) development. Because of their low cost and small size, these devices have the ability to help many developing countries with economic prosperity. The entire aggregation of financial investments in the agricultural area has increased appreciably in recent years. Sooth to say, agriculture remains a massive part of the world’s commercial growth, and due to some complications, the agriculture fields withstand massive losses. Pets and destructive insects seem to be the primary reasons for certain degenerative diseases. It minimizes the potential productivity of the crops. For increasing the quality of the plants, fertilizers and pesticides are appropriately applied. Using UAVs (Unmanned Aerial Vehicles) for spraying pesticides and fertilizing materials is an exuberant contraption. It adequately reduces the rate of health dilemma and the number of workers, which is quite an impressive landmark. Willing producers are also adopting UAVs in agriculture to soil and field analysis, seed sowing, lessen the time and costs correlated with crop scouting, and field mapping. It is rapid, and it can sensibly diminish a farmer’s workload, which is significantly a part of the agricultural revolution. This article aims to proportionally represent the concept of agricultural purposed UAV clear to the neophytes. First, this paper outlines the harmonic framework of the agricultural UAV, and then it abundantly illustrates the methods and materials. Finally, the article portrays the outcome.

RETRACTED: Application of agricultural IoT technology based on 5 G network and FPGA

Personal knowledge of the agricultural cycle the current agribusiness than any other age is more concerned about the information, accuracy and prospects. Based on Internet of Things (IoT), rapid development innovative things has reviewed virtually every industry, including smart agribusiness; the company has transformed its business from quantitative methods. This gradual change has shaken the current comprehensive agricultural technology and open new doors in the problematic border. This article describes the remote sensor and the IoT horticulture functions. The meet up with this innovative farming regular rehearsal, face the challenge to expect. Let's take a closer look at IoT gadgets, gadgets and how to deal with applications related to agriculture experienced remote sensors. Record the sensor in precise agricultural applications can access, such as soil preparations, crop conditions, water systems, insects and irritation testing from planting to harvesting stage throughout the harvesting, crushing and transport, innovative ways to help growers become more and more apparent. This method also considers crop scouting and other significant applications, such as using an automated aircraft to increase crop yields. In justified cases, it will introduce different things and the best model based on the agriculture stage. Finally, according to the in-depth study, goal will recognize agribusiness in the flow of things and a future model and respond to potential research challenges. This article also describes another gain awareness and ideal for the application, such as increasing the use of 5 G network support production. Finally, after careful research to understand the fate of the power of agribusiness and remote sensor model, and potential research model to respond to the challenge. Frame configuration and have a valid attempt Field Programmable Gate Array (FPGA) by site kit. Goal need to consider the speed and the rationalization of the regional planning framework.

Current developments in greenhouse robotics and challenges for the future

Horticultural production faces a growing demand for mechanization, automation and robotics as labour costs are increasing and the availability of skilled labour is decreasing. The modern consumer demands guaranteed and constant quality. Moreover, there are intensified hygiene, food safety and traceability demands. Automated production and quality assessment systems can contribute to fulfil these demands. Many highly automated systems are already applied in commercial high-tech greenhouses. This includes logistics and autonomous transport of plants and harvested product from the greenhouse to the sorting and packing facilities, spraying robots, machine vision based sorting systems for pot-plants and cut-flowers and robotic cutting, planting and grafting machines. Current research focus on automated crop scouting (e.g., insect and disease detection), phenotyping (e.g., monitoring and predicting fruit setting) and postharvest quality assessment. Many research activities are currently carried out on robotic systems that are capable to autonomously harvest crops and to conduct other repetitive and labour intensive operations. In most of these applications the success rate and cycle time are currently still too low to allow implementation in commercial practice. Human-robot collaboration and “human in the loop” applications are important stepping-stones towards full automation. These applications must also include the key issues of user safety and user acceptance. From the plant side, breeding crops with novel phenotypes and plant architectures, such as fruits which are easy to see and reach by robots, will simplify and accelerate the application of robotics in the horticultural field. Challenges involve the complex integration of multiple disciplines and technologies (e.g., navigation, safe operation, grasping, manipulation, perception, learning and adaptation). Current developments are supported by the worldwide rapid improvements in computing and sensor hardware, software and artificial intelligence.

Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet

Rice is a globally important crop that will continue to play an essential role in feeding our world as we grapple with climate change and population growth. Lodging is a primary threat to rice production, decreasing rice yield, and quality. Lodging assessment is a tedious task and requires heavy labor and a long duration due to the vast land areas involved. Newly developed autonomous crop scouting techniques have shown promise in mapping crop fields without any human interaction. By combining autonomous scouting and lodged rice detection with edge computing, it is possible to estimate rice lodging faster and at a much lower cost than previous methods. This study presents an adaptive crop scouting mechanism for Autonomous Unmanned Aerial Vehicles (UAV). We simulate UAV crop scouting of rice fields at multiple levels using deep neural networks and real UAV energy profiles, focusing on areas with high lodging. Using the proposed method, we can scout rice fields 36% faster than conventional scouting methods at 99.25% accuracy.

Crop Surveillance using Unmanned Aerial Vehicle for Precision Agriculture

Precision agriculture (PA) is a combination of latest technologies planned to increase the productivity and profitability by retaining the quality of the fertile land and surrounding environment. Crop monitoring in precision agriculture will be achieved by implementing innovative techniques; however the utilization of Wireless Sensor Network (WSNs) results in low power and low cost utilization arrangements thus turning into a predominant alternative. It is likewise outstanding that harvests are additionally influenced adversely by interlopers (human or animals) and by insufficient control of the production process. Crop surveillance through drone is one of the technical approaches to capture and distinguish the crop patterns, which helps in early detection of the crop damage, leads the farmers to take care for crop yield. Drone is also termed as Unmanned Aerial Vehicle (UAV). These vehicles are equipped with required electronic sensors, cameras and a flight control system to simulate the UAV, with small in size and flexible to handle and operate, These Aerial Vehicles can operate within indoor as well as at outdoor. The goal of the research is to contribute to the implementation and deployment of remote sensing technology with UAV. This paper enumerate on the applications of UAVs for crop scouting and analyzing the transmitted images using NDVI to predict the crop growth and yield.

Crop scouting using UAV imagery: a case study for potatoes

Crop scouting is essential to manage crops such as potatoes and to detect stresses. In fact, conventional approaches require a lot of time and staff. The rise of unmanned aerial vehicle imagery offers interesting perspectives in this area. However, the development of interpreted and generalizable cartographic products that can be used directly by producers still poses challenges in terms of processing complexity and production time. The purpose of this study was to develop a tool for the phytosanitary surveillance of potato crops using scouting maps in support of conventional methods. The approach is based on relatively simple and efficient preprocessing methods when no radiometric correction data are available. The first step establishes the best correlations between some biophysical parameters directly related to phytosanitary problems and vegetation indices in the visible and infrared domains. The second step allows the development of an approach to classify the following three stresses: pests, diseases, and development problems. Validated by scouting field sites, the developed approach makes it possible to quickly produce accurate scouting maps that producers can use directly thanks to their high potential for generalization to other areas and crop productions.

Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence

Specialty crop growers face challenges from numerous diseases and pests. For example, the Asian citrus psyllid (ACP) is a key pest of citrus due to its role as vector of huanglongbing (HLB) (greening disease). There is no known cure for HLB, but vector management is critical, both for slowing spread and attenuating symptoms in infected trees. Therefore, monitoring ACP population, as well as other pest populations, is an essential management component for timing and assessment of control actions. Manual crop scouting is often labor intensive and time consuming. In this work, an automated system was developed and evaluated utilizing machine vision and artificial intelligence to monitor ACP in groves. This system comprised a tapping mechanism to collect insects from the tree’s branches and a board with a grid of cameras for image acquisition. A software was developed using two convolutional neural-networks to accurately detect and distinguish psyllids from other insects and debris fallen from the tree. A GPS was utilized to automatically record individual tree position to facilitate data assessment on large groves. A precision and recall of 80% and 95%, respectively, was obtained on detecting ACPs on a sample of 90 young citrus trees. The system proved a great potential to automate scouting procedures in citrus and to be extended to other crop insects.

Early detection of arthropod-induced stress in strawberry using innovative remote sensing technology

Event Abstract Back to Event Early detection of arthropod-induced stress in strawberry using innovative remote sensing technology Elvira S. De Lange1* and Christian Nansen1 1 University of California, Davis, United States Innovative imaging technologies are being promoted to automate and optimize crop scouting and detection of emerging pest outbreaks. Due to the large size of farm operations, pest monitoring is time consuming; pests are often detected late, allowing populations to increase. Early, accurate pest detection is key for effective management and control. Spectral imaging, or remote sensing, can be used to indirectly detect pest presence, through stress-induced changes in leaf reflectance (1). However, only a limited number of studies showed that leaf reflectance data could be used to differentiate between stressors (2, 3). We aim to develop a feasible, cost-effective, accurate method for early detection of stress induced by different pests in strawberry, using innovative airborne remote sensing technology. We hypothesized that herbivores with different feeding biology cause significantly different leaf reflectance responses. To address this hypothesis, potted strawberry plants were experimentally infested with three arthropod pests with different feeding modes: two-spotted spider mite, a sucking pest; silverleaf whitefly, a piercing-sucking pest; and beet armyworm, a chewing pest. Control plants remained non-infested. In a greenhouse setting, we acquired data on leaf reflectance over a 14-day period with a novel, greenhouse-suitable setting, containing a hyperspectral camera mounted onto a rail system. Furthermore, in a commercial strawberry field, we acquired data on leaf reflectance using the same hyperspectral camera, mounted on a drone (4). Leaf samples were collected and inspected for arthropod presence or absence. Results showed that in a greenhouse setting, leaf reflectance profiles differed between strawberry plants exposed to feeding by the different arthropod herbivores. In a field setting, leaf reflectance profiles differed between areas in the presence or absence of two-spotted spider mite. These results indicate that reflectance in specific wavelengths can be used to detect early pest infestations in strawberry. Current experiments involve expanded greenhouse trails, aiming to distinguish stress caused by biotic and abiotic stressors in strawberry. Also, we’re performing additional field trials at local strawberry farms. Ultimately, we aim to develop a remote sensing-based decision support tool for improved sustainable pest management on commercial strawberry farms. Acknowledgements We thank Alison Stewart, Machiko Murdock, Robert Starnes, April Van Hise, Keshav Singh, Charissa Tseng, Yasmeen Haider, and Rachel Purington (UC Davis) for technical assistance. We also thank Mark Edsall, Daniel Olivier (California Strawberry Commission) and Hillary Thomas (Naturipe Berry Growers), and the commercial growers who made their fields available. This research is supported by Western Sustainable Agriculture Research and Education (project SW17-060, http://www.westernsare.org/).

Preparing Youths for Careers in Agriculture Through State Crop Scouting Competitions

State crop scouting competitions (CSCs) promote agriculture by introducing youths in Indiana, Iowa, and Nebraska to various agricultural disciplines while focusing on integrated pest management (IPM). High school students compete as teams to address crop management issues at various stations. Each station is led by university representatives. Two surveys were conducted to determine the impacts of the competition on students. Results indicate that students improved in skills key to future careers and that they learned about aspects of IPM. CSCs can serve as models for states that wish to improve ties between university-based Extension specialists and state high schools.

Task-based agricultural mobile robots in arable farming: A review

In agriculture (in the context of this paper, the terms “agriculture” and “farming” refer to only the farming of crops and exclude the farming of animals), smart farming and automated agricultural technology have emerged as promising methodologies for increasing the crop productivity without sacrificing produce quality. The emergence of various robotics technologies has facilitated the application of these techniques in agricultural processes. However, incorporating this technology in farms has proven to be challenging because of the large variations in shape, size, rate and type of growth, type of produce, and environmental requirements for different types of crops. Agricultural processes are chains of systematic, repetitive, and time-dependent tasks. However, some agricultural processes differ based on the type of farming, namely permanent crop farming and arable farming. Permanent crop farming includes permanent crops or woody plants such as orchards and vineyards whereas arable farmingincludestemporary crops such as wheat and rice. Major operations in open arable farming include tilling, soil analysis, seeding, transplanting, crop scouting, pest control, weed removal and harvesting and robots can assist in performing all of these tasks. Each specific operation requires axillary devices and sensors with specific functions. This article reviews the latest advances in the application of mobile robots in these agricultural operations for open arable farming and provide an overview of the systems and techniques that are used. This article also discusses various challenges for future improvements in using reliable mobile robots for arable farming.

Estimating Triticale Dry Matter Yield in Parcel Plot Trials From Aerial and Ground Based Spectral Measurements

Abstract: Spectral measurement devices are a useful tool for quantifying biomass and the nitrogen uptake of plants. They are employed for site specific nitrogen application in arable farming and for crop scouting in parcel plot trials. In 2014 a plot trial with varying fertilizer types and levels has been monitored with a low-cost handheld NDVI sensor and an UAV carrying a multispectral camera. The results show that data from both spectral measurement devices was fit to estimate triticale dry matter yield with confidence levels exceeding 95%.

A Socio-technical Model for Analyzing Safety Risk of Unmanned Aircraft Systems (UAS): An Application to Precision Agriculture

There are a number of concerns about the introduction of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS) of which safety risk is of paramount importance. For UAS that typically fly low and slow, the possibility of a mid-air collision with a nearby general aviation aircraft needs to be studied by identifying possible hazards and assessing mitigations. The Aviation System Risk Model (ASRM) is a first-generation socio-technical model that uses a Bayesian Belief Network (BBN) methodology to integrate possible hazards to assess a non-linear safety risk metric. The ASRM may be used to evaluate underlying causal factors linked to the vehicle and/or to the systems and procedures that led to the unsafe state and the interactions among these factors that contributed to the safety risk. The ASRM can also assess the projected impact of mitigations. The ASRM facilitates robust inductive reasoning on the hypothesized accident scenarios, ideal for addressing emergent NAS operations where there may be obvious data and experience limitations. Recently, the ASRM has been updated with the use of the Hazard Classification and Analysis System (HCAS) that provides analytic structure for categorizing hazards related to the UAS, Airmen, Operations and the Environment. In this paper, the ASRM, together with the HCAS, is demonstrated with a precision agriculture application of a notional scenario that involves an UAS being used for crop scouting. It is conjectured that the UAS interacts with a neighboring farm where a conventional piloted cropduster is being used for pesticide spraying. The UAS being used is a rotorcraft-type where there is a failure of the separation assurance function since the UAS leaves its Area of Operation (AO) due to a Ground Control Station (GCS) transmission disruption (from faulty maintenance) and by the waypoints being incorrectly programmed. The ASRM is used to analyze such a scenario leading to a collision volume being entered by both the UAS and the piloted cropduster that possibly leads to a mid-air collision. The efficacy of a geofence or a “virtual barrier” mitigation for the UAS is also analyzed. The ASRM safety risk results for this notional scenario are presented and interpreted.

Applications of low altitude remote sensing in agriculture upon farmers' requests--a case study in northeastern Ontario, Canada.

With the growth of the low altitude remote sensing (LARS) industry in recent years, their practical application in precision agriculture seems all the more possible. However, only a few scientists have reported using LARS to monitor crop conditions. Moreover, there have been concerns regarding the feasibility of such systems for producers given the issues related to the post-processing of images, technical expertise, and timely delivery of information. The purpose of this study is to showcase actual requests by farmers to monitor crop conditions in their fields using an unmanned aerial vehicle (UAV). Working in collaboration with farmers in northeastern Ontario, we use optical and near-infrared imagery to monitor fertilizer trials, conduct crop scouting and map field tile drainage. We demonstrate that LARS imagery has many practical applications. However, several obstacles remain, including the costs associated with both the LARS system and the image processing software, the extent of professional training required to operate the LARS and to process the imagery, and the influence from local weather conditions (e.g. clouds, wind) on image acquisition all need to be considered. Consequently, at present a feasible solution for producers might be the use of LARS service provided by private consultants or in collaboration with LARS scientific research teams.