Hasil untuk "Agriculture"

A. Krueger

Steven Moonen, Rob Salaets, Kenneth Batstone et al.

In the manufacturing industry, computer vision systems based on artificial intelligence (AI) are widely used to reduce costs and increase production. Training these AI models requires a large amount of training data that is costly to acquire and annotate, especially in high-variance, low-volume manufacturing environments. A popular approach to reduce the need for real data is the use of synthetic data that is generated by leveraging computer-aided design (CAD) models available in the industry. However, in the agricultural industry these models are not readily available, increasing the difficulty in leveraging synthetic data. In this paper, we present different techniques for substituting CAD files to create synthetic datasets. We measure their relative performance when used to train an AI object detection model to separate stones and potatoes in a bin picking environment. We demonstrate that using highly representative 3D models acquired by scanning or using image-to-3D approaches can be used to generate synthetic data for training object detection models. Finetuning on a small real dataset can significantly improve the performance of the models and even get similar performance when less representative models are used.

Yu Dai

Karen Wolf, Pierre Fernique, Hans-Peter Piepho

A common assumption in the spatial analysis of agricultural field trials is stationarity. In practice, however, this assumption is often violated due to unaccounted field effects. For instance, in plant breeding field trials, this can lead to inaccurate estimates of plant performance. Based on such inaccurate estimates, breeders may be impeded in selecting the best performing plant varieties, slowing breeding progress. We propose a method to automatically verify the hypothesis of stationarity. The method is sensitive towards mean as well as variance-covariance nonstationarity. It is specifically developed for the two-dimensional grid-structure of field trials. The method relies on the hypothesis that we can detect nonstationarity by partitioning the field into areas, within which stationarity holds. We applied the method to a large number of simulated datasets and a real-data example. The method reliably points out which trials exhibit quality issues and gives an indication about the severity of nonstationarity. This information can significantly reduce the time spent on manual quality control and enhance its overall reliability. Furthermore, the output of the method can be used to improve the analysis of conducted trials as well as the experimental design of future trials.

Thevathayarajh Thayananthan, Xin Zhang, Yanbo Huang et al.

Cotton is a major cash crop in the United States, with the country being a leading global producer and exporter. Nearly all U.S. cotton is grown in the Cotton Belt, spanning 17 states in the southern region. Harvesting remains a critical yet challenging stage, impacted by the use of costly, environmentally harmful defoliants and heavy, expensive cotton pickers. These factors contribute to yield loss, reduced fiber quality, and soil compaction, which collectively threaten long-term sustainability. To address these issues, this study proposes a lightweight, small-scale, vision-guided autonomous robotic cotton picker as an alternative. An autonomous system, built on Clearpath's Husky platform and integrated with the CottonEye perception system, was developed and tested in the Gazebo simulation environment. A virtual cotton field was designed to facilitate autonomous navigation testing. The navigation system used Global Positioning System (GPS) and map-based guidance, assisted by an RGBdepth camera and a YOLOv8nseg instance segmentation model. The model achieved a mean Average Precision (mAP) of 85.2%, a recall of 88.9%, and a precision of 93.0%. The GPS-based approach reached a 100% completion rate (CR) within a $(5e-6)^{\circ}$ threshold, while the map-based method achieved a 96.7% CR within a 0.25 m threshold. The developed Robot Operating System (ROS) packages enable robust simulation of autonomous cotton picking, offering a scalable baseline for future agricultural robotics. CottonSim code and datasets are publicly available on GitHub: https://github.com/imtheva/CottonSim

Sajad Ahmadi, Mohammadreza Davoodi, Javad Mohammadpour Velni

This paper presents an adaptive coverage control method for a fleet of off-road and Unmanned Ground Vehicles (UGVs) operating in dynamic (time-varying) agricultural environments. Traditional coverage control approaches often assume static conditions, making them unsuitable for real-world farming scenarios where obstacles, such as moving machinery and uneven terrains, create continuous challenges. To address this, we propose a real-time path planning framework that integrates Unmanned Aerial Vehicles (UAVs) for obstacle detection and terrain assessment, allowing UGVs to dynamically adjust their coverage paths. The environment is modeled as a weighted directed graph, where the edge weights are continuously updated based on the UAV observations to reflect obstacle motion and terrain variations. The proposed approach incorporates Voronoi-based partitioning, adaptive edge weight assignment, and cost-based path optimization to enhance navigation efficiency. Simulation results demonstrate the effectiveness of the proposed method in improving path planning, reducing traversal costs, and maintaining robust coverage in the presence of dynamic obstacles and muddy terrains.

Qi Cheng, Licheng Liu, Yao Zhang et al.

Agricultural monitoring is critical for ensuring food security, maintaining sustainable farming practices, informing policies on mitigating food shortage, and managing greenhouse gas emissions. Traditional process-based physical models are often designed and implemented for specific situations, and their parameters could also be highly uncertain. In contrast, data-driven models often use black-box structures and does not explicitly model the inter-dependence between different ecological variables. As a result, they require extensive training data and lack generalizability to different tasks with data distribution shifts and inconsistent observed variables. To address the need for more universal models, we propose a knowledge-guided encoder-decoder model, which can predict key crop variables by leveraging knowledge of underlying processes from multiple physical models. The proposed method also integrates a language model to process complex and inconsistent inputs and also utilizes it to implement a model selection mechanism for selectively combining the knowledge from different physical models. Our evaluations on predicting carbon and nitrogen fluxes for multiple sites demonstrate the effectiveness and robustness of the proposed model under various scenarios.

Rick van Essen, Eldert van Henten, Lammert Kooistra et al.

This paper presents an adaptive path planner for object search in agricultural fields using UAVs. The path planner uses a high-altitude coverage flight path and plans additional low-altitude inspections when the detection network is uncertain. The path planner was evaluated in an offline simulation environment containing real-world images. We trained a YOLOv8 detection network to detect artificial plants placed in grass fields to showcase the potential of our path planner. We evaluated the effect of different detection certainty measures, optimized the path planning parameters, investigated the effects of localization errors, and different numbers of objects in the field. The YOLOv8 detection confidence worked best to differentiate between true and false positive detections and was therefore used in the adaptive planner. The optimal parameters of the path planner depended on the distribution of objects in the field. When the objects were uniformly distributed, more low-altitude inspections were needed compared to a non-uniform distribution of objects, resulting in a longer path length. The adaptive planner proved to be robust against localization uncertainty. When increasing the number of objects, the flight path length increased, especially when the objects were uniformly distributed. When the objects were non-uniformly distributed, the adaptive path planner yielded a shorter path than a low-altitude coverage path, even with a high number of objects. Overall, the presented adaptive path planner allowed finding non-uniformly distributed objects in a field faster than a coverage path planner and resulted in a compatible detection accuracy. The path planner is made available at https://github.com/wur-abe/uav_adaptive_planner.

Y. Tey, M. Brindal

L. Afsah-Hejri, E. Akbari, Arash Toudeshki et al.

Abstract Terahertz (THz) waves are non-ionizing radiations with unique properties of both microwave and infrared. THz imaging and spectroscopy have been widely used for non-destructive testing, security screening, medical imaging, and quality control both in the agricultural and food industries. This review briefly describes the principles of how THz is generated, discusses its current agricultural applications and highlights the research gaps. THz spectroscopy has primarily been used for measuring the water content of plant leaves. Later, it was used to detect dead and live insects and pests in agricultural products. A combination of THz spectroscopy with chemometric methods, machine learning, and search algorithms helped scientists to construct classification models for discrimination of transgenic seeds, pesticides, harmful compounds, and poisonous plants. THz spectroscopy has also been used for soil inspection and detection of heavy metals and buried objects. In recent years, there have been significant technological improvements in developing THz sources and detectors, which enabled researchers to perform ultra-fast scanning and obtain high-resolution images. Using these new THz technologies, researchers can differentiate between fresh and old leaves, monitor the water status of plants, and estimate the crop yield. Although THz has proved to be a useful tool for non-destructive quality control of agricultural products and soil inspection, the technique has some limitations such as a low limit of detection (LOD) for pesticides, low spatial resolution, and limited penetration. It can also be influenced by the physical properties of samples such as particle size, and surface roughness. On the sensor side, more research is needed to improve the performance of THz systems, reduce THz sensor costs and ruggedness of the system for applications in agriculture. Also, more research is required to explore new potential applications of this technology in agriculture.

K. A. Bybee‐Finley, M. Ryan

Sustainable intensification calls for agroecological and adaptive management of the agrifood system. Here, we focus on intercropping and how this agroecological practice can be used to increase the sustainability of crop production. Strip, mixed, and relay intercropping can be used to increase crop yields through resource partitioning and facilitation. In addition to achieving greater productivity, diversifying cropping systems through the use of strategic intercrops can increase yield stability, reduce pests, and improve soil health. Several intercropping systems are already implemented in industrialized agricultural landscapes, including mixed intercropping with perennial grasses and legumes as forage and relay intercropping with winter wheat and red clover. Because intercropping can provide numerous benefits, researchers should be clear about their objectives and use appropriate methods so as to not draw spurious conclusions when studying intercrops. In order to advance the practice, experiments that test the effects of intercropping should use standardized methodology, and researchers should report a set of common criteria to facilitate cross-study comparisons. Intercropping with two or more crops appears to be less common with annuals than perennials, which is likely due to differences in the mechanisms responsible for complementarity. One area where intercropping with annuals in industrialized agricultural landscapes has advanced is with cover crops, where private, public, and governmental organizations have harmonized efforts to increase the adoption of cover crop mixtures.

Mingke Lu, Han Gao, Haijie Dai et al.

We propose a Path-Tracking Hybrid A* planner coupled with a hierarchical Model Predictive Control (MPC) framework for path smoothing in agricultural vehicles. The goal is to minimize deviation from reference paths during cross-furrow operations, thereby optimizing operational efficiency, preventing crop and soil damage, while also enforcing curvature constraints and ensuring full-body collision avoidance. Our contributions are threefold: (1) We develop the Path-Tracking Hybrid A* algorithm to generate smooth trajectories that closely adhere to the reference trajectory, respect strict curvature constraints, and satisfy full-body collision avoidance. The adherence is achieved by designing novel cost and heuristic functions to minimize tracking errors under nonholonomic constraints. (2) We introduce an online replanning strategy as an extension that enables real-time avoidance of unforeseen obstacles, while leveraging pruning techniques to enhance computational efficiency. (3) We design a hierarchical MPC framework that ensures tight path adherence and real-time satisfaction of vehicle constraints, including nonholonomic dynamics and full-body collision avoidance. By using linearized MPC to warm-start the nonlinear solver, the framework improves the convergence of nonlinear optimization with minimal loss in accuracy. Simulations on real-world farm datasets demonstrate superior performance compared to baseline methods in safety, path adherence, computation speed, and real-time obstacle avoidance.

Abhijin Adiga, Yohai Trabelsi, Tanvir Ferdousi et al.

Optimal allocation of agricultural water in the event of droughts is an important global problem. In addressing this problem, many aspects, including the welfare of farmers, the economy, and the environment, must be considered. Under this backdrop, our work focuses on several resource-matching problems accounting for agents with multi-crop portfolios, geographic constraints, and fairness. First, we address a matching problem where the goal is to maximize a welfare function in two-sided markets where buyers' requirements and sellers' supplies are represented by value functions that assign prices (or costs) to specified volumes of water. For the setting where the value functions satisfy certain monotonicity properties, we present an efficient algorithm that maximizes a social welfare function. When there are minimum water requirement constraints, we present a randomized algorithm which ensures that the constraints are satisfied in expectation. For a single seller--multiple buyers setting with fairness constraints, we design an efficient algorithm that maximizes the minimum level of satisfaction of any buyer. We also present computational complexity results that highlight the limits on the generalizability of our results. We evaluate the algorithms developed in our work with experiments on both real-world and synthetic data sets with respect to drought severity, value functions, and seniority of agents.

Dong Sheng, Siyuan Jing, Xueqing He et al.

Biodiversity-associated ecosystem services such as pollination and biocontrol may be severely affected by emerging nano/micro-plastics (NMP) pollution. We synthesized the little-explored effects of NMP on pollinators and biocontrol agents on the organismal, farm and landscape scale. For instance ingested NMP trigger organismal changes from gene expression, organ damage to behavior modifications. At the farm and landscape level, NMP will likely amplify synergistic effects with other threats such as pathogens and antibiotics, and may alter landscape properties such as floral resource distributions in high NMP concentration areas, what we call NMP islands. It is essential to understand the functional exposure pathways of NMP on pollinators and biocontrol agents to comprehensively evaluate the risks for agricultural ecosystems and global food security.

Usman A. Zahidi, Krystian Łukasik, Grzegorz Cielniak

The maturity classification of specialty crops such as strawberries and tomatoes is an essential agricultural downstream activity for selective harvesting and quality control (QC) at production and packaging sites. Recent advancements in Deep Learning (DL) have produced encouraging results in color images for maturity classification applications. However, hyperspectral imaging (HSI) outperforms methods based on color vision. Multivariate analysis methods and Convolutional Neural Networks (CNN) deliver promising results; however, a large amount of input data and the associated preprocessing requirements cause hindrances in practical application. Conventionally, the reflectance intensity in a given electromagnetic spectrum is employed in estimating fruit maturity. We present a feature extraction method to empirically demonstrate that the peak reflectance in subbands such as 500-670 nm (pigment band) and the wavelength of the peak position, and contrarily, the trough reflectance and its corresponding wavelength within 671-790 nm (chlorophyll band) are convenient to compute yet distinctive features for the maturity classification. The proposed feature selection method is beneficial because preprocessing, such as dimensionality reduction, is avoided before every prediction. The feature set is designed to capture these traits. The best SOTA methods, among 3D-CNN, 1D-CNN, and SVM, achieve at most 90.0 % accuracy for strawberries and 92.0 % for tomatoes on our dataset. Results show that the proposed method outperforms the SOTA as it yields an accuracy above 98.0 % in strawberry and 96.0 % in tomato classification. A comparative analysis of the time efficiency of these methods is also conducted, which shows the proposed method performs prediction at 13 Frames Per Second (FPS) compared to the maximum 1.16 FPS attained by the full-spectrum SVM classifier.

Mahsa Khosravi, Matthew Carroll, Kai Liang Tan et al.

Agricultural production requires careful management of inputs such as fungicides, insecticides, and herbicides to ensure a successful crop that is high-yielding, profitable, and of superior seed quality. Current state-of-the-art field crop management relies on coarse-scale crop management strategies, where entire fields are sprayed with pest and disease-controlling chemicals, leading to increased cost and sub-optimal soil and crop management. To overcome these challenges and optimize crop production, we utilize machine learning tools within a virtual field environment to generate localized management plans for farmers to manage biotic threats while maximizing profits. Specifically, we present AgGym, a modular, crop and stress agnostic simulation framework to model the spread of biotic stresses in a field and estimate yield losses with and without chemical treatments. Our validation with real data shows that AgGym can be customized with limited data to simulate yield outcomes under various biotic stress conditions. We further demonstrate that deep reinforcement learning (RL) policies can be trained using AgGym for designing ultra-precise biotic stress mitigation strategies with potential to increase yield recovery with less chemicals and lower cost. Our proposed framework enables personalized decision support that can transform biotic stress management from being schedule based and reactive to opportunistic and prescriptive. We also release the AgGym software implementation as a community resource and invite experts to contribute to this open-sourced publicly available modular environment framework. The source code can be accessed at: https://github.com/SCSLabISU/AgGym.

Tahir Abbas KHAN, Hadiqa HASSAN, Haocheng WANG et al.

Drought stress poses a significant challenge to agriculture sustainability across the globe. Drought stress negatively affects the plant growth and productivity and the intensity of this serious abiotic stress is continuously increasing which is a serious threat across the globe. Different measures are being used to mitigate the adverse impacts of drought stress. Among these measures, the application of exogenous osmolytes and growth hormones is considered an important way to mitigate the adverse impacts of drought. Recently, jasmonic acid (JA) has emerged as an excellent growth hormone to improve drought tolerance owing to its involvement in different plant physiological and biochemical processes. Jasmonic acid improves membrane stability plant water relations, nutrient uptake, osmolyte accumulation, and antioxidant activities that can counter the toxic effects of drought. It also contributes to signaling pathways, i.e., genes network, stress-responsive proteins, signaling intermediates, and enzymes that protect the plants from the toxic effects of drought. Further, JA also protects and maintains the integrity of plant cells by up-regulating the antioxidant defense system and increasing osmolyte accumulation. In this review, we have documented the protective role of JA under drought stress. The various mechanisms of JA in inducing drought tolerance are discussed and different research gaps are also identified. This review will help the readers to learn more about the role of JA to mitigate the toxic effects of drought and it will provide new knowledge to develop the drought tolerance in plants.

Mohammad Erfatpour, Dustin MacLean, Rachid Lahlali et al.

The ovule is a plant structure that upon fertilization, transforms into a seed. Successful fertilization is required for optimum crop productivity and is strongly affected by environmental conditions including temperature and precipitation. Climate change refers to sustained changes in global or regional climate patterns over an extended period, typically decades to millions of years. These shifts can result from natural processes like volcanic eruptions and solar radiation fluctuations, but in recent times, human activities—especially the burning of fossil fuels, deforestation, and industrial emissions—have accelerated the pace and scale of climate change. Human-induced climate change impacts the agricultural sector mainly through global warming and altering weather patterns, both of which create conditions that challenge agricultural production and food security. With food demand projected to sharply increase by 2050, urgent action is needed to prevent the worst impacts of climate change on food security and allow time for agricultural production systems to adapt and become more resilient. Gaining insights into the female reproductive part of the flower and seed development under extreme environmental conditions is important to oversee plant evolution, agricultural productivity, and food security in the face of climate change. This review summarizes the current knowledge on plant reproductive development and the effects of temperature and water stress, soil salinity, elevated carbon dioxide, and ozone pollution on the female reproductive structure and development across grain legumes, cereal, oilseed, and horticultural crops. It identifies gaps in existing studies for potential future research and suggests suitable mitigation strategies for sustaining crop productivity in a changing climate.

G. Roesch-McNally, A. Basche, J. Arbuckle et al.

Abstract Cover crops are known to promote many aspects of soil and water quality, yet estimates find that in 2012 only 2.3% of the total agricultural lands in the Midwestern USA were using cover crops. Focus groups were conducted across the Corn Belt state of Iowa to better understand how farmers confront barriers to cover crop adoption in highly intensive agricultural production systems. Although much prior research has focused on analyzing factors that help predict cover crop use on farms, there is limited research on how farmers navigate and overcome field-level (e.g. proper planting of a cover crop) and structural barriers (e.g. market forces) associated with the use of cover crops. The results from the analysis of these conversations suggest that there is a complex dialectical relationship between farmers' individual management decisions and the broader agricultural context in the region that constrains their decisions. Farmers in these focus groups shared how they navigate complex management decisions within a generally homogenized agricultural and economic landscape that makes cover crop integration challenging. Many who joined the focus groups have found ways to overcome barriers and successfully integrate cover crops into their cropping systems. This is illustrated through farmers' descriptions of their ‘whole system’ approach to cover crops management, where they described how they prioritize the success of their cover crops by focusing on multiple aspects of management, including changes they have made to nutrient application and modifications to equipment. These producers also engage with farmer networks to gain strategies for overcoming management challenges associated with cover crops. Although many participants had successfully planted cover crops, they tended to believe that greater economic incentives and/or more diverse crop and livestock markets would be needed to spur more widespread adoption of the practice. Our results further illustrate how structural and field-level barriers constrain individual actions, as it is not simply the basic agronomic considerations (such as seeding and terminating cover crops) that pose a challenge to their use, but also the broader economic and market drivers that exist in agriculturally intensive systems. Our study provides evidence that reducing structural barriers to adoption may be necessary to increase the use of this conservation practice to reduce environmental impacts associated with intensive agricultural production.

T. Duong, T. Brewer, J. Luck et al.

Farmers around the world face and manage a wide range of enterprise-related risks. These risks are increasing due to a range of factors including globalisation, increased trade in agricultural products, and climate change, jeopardising agricultural enterprises and forcing farmers to adjust their production and management strategies. Here we present results of a systematic literature review, following PRISMA protocol, of farmers’ perceptions of, and responses to, agricultural risks. Using data reduction method (factor analysis) and descriptive statistics, we analysed 197 studies and found that weather-related risk (55%), biosecurity threats (48%), and human risk (35%) are the significant risks perceived by farmers for their agricultural enterprises. Diversification of crop and animal production (28%) and pests and diseases monitoring and prevention (20%) were the preferred agricultural risk management strategies employed by farmers. Few studies have investigated socio-economic factors that explain risk perceptions (18%) or factors that influence how farmers manage agricultural risks (11%). The main barriers to successfully managing agricultural risks were limited access to information and formal low-interest loan systems, especially in developing countries. We identified a mismatch between perceived risk sources and risk management strategies, highlighting a need to improve understanding of why particular management responses are employed to address the various risks. This review suggests areas for future research to improve understanding of the perceptions of risks held by farmers, and to support efforts to manage and reduce these risks.