LEDs for photons, physiology and food.

Lighting based on light-emitting diodes (LEDs) not only is more energy efficient than traditional lighting, but also enables improved performance and control. The colour, intensity and distribution of light can now be controlled with unprecedented precision, enabling light to be used both as a signal for specific physiological responses in humans and plants, and as an efficient fuel for fresh food production. Here we show how a broad and improved understanding of the physiological responses to light will facilitate greater energy savings and provide health and productivity benefits that have not previously been associated with lighting.

From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management.

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.

Streamlining YOLOv7 for Rapid and Accurate Detection of Rapeseed Varieties on Embedded Device.

Real-time seed detection on resource-constrained embedded devices is essential for the agriculture industry and crop yield. However, traditional seed variety detection methods either suffer from low accuracy or cannot directly run on embedded devices with desirable real-time performance. In this paper, we focus on the detection of rapeseed varieties and design a dual-dimensional (spatial and channel) pruning method to lighten the YOLOv7 (a popular object detection model based on deep learning). We design experiments to prove the effectiveness of the spatial dimension pruning strategy. And after evaluating three different channel pruning methods, we select the custom ratio layer-by-layer pruning, which offers the best performance for the model. The results show that using custom ratio layer-by-layer pruning can achieve the best model performance. Compared to the YOLOv7 model, this approach results in mAP increasing from 96.68% to 96.89%, the number of parameters reducing from 36.5 M to 9.19 M, and the inference time per image on the Raspberry Pi 4B reducing from 4.48 s to 1.18 s. Overall, our model is suitable for deployment on embedded devices and can perform real-time detection tasks accurately and efficiently in various application scenarios.

Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture.

Musculoskeletal Disorders (MSDs) stand as a prominent cause of injuries in modern agriculture. Scientific research has highlighted a causal link between MSDs and awkward working postures. Several methods for the evaluation of working postures, and related risks, have been developed such as the Rapid Upper Limb Assessment (RULA). Nevertheless, these methods are generally applied with manual measurements on pictures or videos. As a consequence, their applicability could be scarce, and their effectiveness could be limited. The use of wearable sensors to collect kinetic data could facilitate the use of these methods for risk assessment. Nevertheless, the existing system may not be usable in the agricultural and vine sectors because of its cost, robustness and versatility to the various anthropometric characteristics of workers. The aim of this study was to develop a technology capable of collecting accurate data about uncomfortable postures and repetitive movements typical of vine workers. Specific objectives of the project were the development of a low-cost, robust, and wearable device, which could measure data about wrist angles and workers' hand positions during possible viticultural operations. Furthermore, the project was meant to test its use to evaluate incongruous postures and repetitive movements of workers' hand positions during pruning operations in vineyard. The developed sensor had 3-axis accelerometers and a gyroscope, and it could monitor the positions of the hand-wrist-forearm musculoskeletal system when moving. When such a sensor was applied to the study of a real case, such as the pruning of a vines, it permitted the evaluation of a simulated sequence of pruning and the quantification of the levels of risk induced by this type of agricultural activity.

Comparison of Depth-Specific Prediction of Soil Properties: MIR vs. Vis-NIR Spectroscopy.

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500-25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0-100 cm) or the shallow layers (0-40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350-2500 nm). A total of 90 soil samples containing 450 subsamples (0-10 cm, 10-20 cm, 20-40 cm, 40-70 cm, and 70-100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07-3.82 g/kg, RPD = 1.10-5.80; TN: RMSEp = 0.11-0.15 g/kg, RPD = 1.70-4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75-8.95 g/kg, RPD = 0.50-3.61; TN: RMSEp = 0.12-0.27 g/kg; RPD = 1.00-3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09-2.97 g/kg, RPD = 2.13-5.80; TN: RMSEp = 0.08-0.19 g/kg, RPD = 1.86-4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07-8.95 g/kg, RPD = 0.50-3.93; TN: RMSEp = 0.11-0.27 g/kg, RPD = 1.00-2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.

Low-Cost Live Insect Scouting Drone: iDrone Bee.

Unmanned aerial vehicles (UAVs, e.g., drones) are a common tool for many civil applications, including precision agriculture, transportation, delivery services, rescue missions, law enforcement, and more. Remote sensing technologies used in conjunction with drones are a dominant application in precision agriculture. Multispectral instrumentation attached to UAVs allows the user to observe multiple parameters, including the normalized difference vegetation index which can represent crop stresses induced by various factors (e.g., drought, insect outbreak, nutrient loss, and other diseases). However, little research has been done to apply drones to accomplish a mission-oriented actionable task in agriculture, such as insect sampling. We propose a low-cost, open source-based live insect scouting drone named 'iDrone Bee' to benefit the integrated pest management (IPM) community by minimizing time and efforts of human interventions while collecting live insects in agricultural fields. Herein we present instruction and operation procedures to build and operate an iDrone Bee for insect scouting in an agricultural ecosystem and validate the system in an alfalfa seed field. The findings of this investigation demonstrate that a drone-based insect scouting method may be a valuable tool to benefit the IPM community.

Unusual Presentation of Bilateral Carotid Artery Dissection: The Importance of Reasoning Outside the Box.

A bilateral internal carotid artery dissection presenting with atypical symptoms of cerebral hypoperfusion has been rarely reported, especially in the absence of obvious precipitating factors. A middle-aged woman presented to the emergency department with a 2-day-history of progressive left arm numbness and weakness, confusion, disorientation and clumsiness worsened by upright position. A cerebral hypoperfusion condition was hypothesized and confirmed by a CT angiography, which showed bilateral internal carotid dissection with uncertain etiology. Screening for predisposing conditions to spontaneous carotid arteries dissection was basically negative. Regarding potential precipitating factors, the patient had used an electric olive harvester days before symptoms onset, without any painful sensation or sudden sequelae. Portable harvesters in olive growing transmit vibrations to the hand-arm system but it remains to be elucidated if hand-arm vibrations can be implicated in vessels wall injury and dissection. Bilateral carotid artery dissection is an infrequent and life-threatening condition which can rarely present with non-specific symptoms of cerebral hypoperfusion. The absence of typical symptoms and known precipitating factors can made the diagnosis quite hard to achieve.

Microencapsulated soil conditioner with a water-soluble core: improving soil nutrition of crop root.

Traditional level of fertilisers was used by most farmers in China with the risks about resources wasting, environmental pollution together with soil structure deterioration. It is practicable to tackle the challenges about over-fertilisation and low efficiency with microencapsulated soil conditioner (MSC), which clads the water soluble core with natural polymer. Fulvic acid (FA) can be used as core material, because it possesses the characteristics of water-soluble, fertiliser maintenance and expedient monitoring. The morphology, structure, and properties of MSC were studied and compared. The particle size of MSC was ranged from 1.58 to 2.14 mm with a similar shape which was obtained by conventional measuring method due to their soft features. This was mainly attributed to the concentration of liquid paraffin and the interaction between shell materials and calcium chloride. FTIR spectra showed that a peak appeared at 1372 cm-1, and this was ascribed to the microcapsules crosslinked and solidified by calcium ions. Sustained release experiment revealed that the microcapsules owned better fertiliser-retaining and water-retaining performances, and FA may be released as long as 750 h. Biodegradation experiments revealed that an obvious pore structure was found on the surface of microspheres after 30 d of degradation, and this was consistent with the sustained release experiment. Pot experiment illustrated that the plants cured with the microcapsules showed significant growth trend and grew up to 9.2 cm with a maximum rate, and this revealed that MSC owned better performance of promoting the growth of crop root.

Nanotechnology: recent advances in viticulture and enology.

Nowadays, nanoscience is a leading modern science that has a major impact on the food, pharmaceutical, and agriculture sectors. Several nanomaterials show a great potential for use during vine growing and winemaking processes. In viticulture, nanotechnology can be applied to protect vines against phytopathogens and to improve grape yield and quality. Thus, nanotechnology may allow the use of lesser amounts of phytochemical compounds, reducing environmental impact and promoting a more sustainable agriculture. And in winemaking, nanomaterials and nanodevices can be used to control the growth of spoilage microorganisms and to reduce or remove undesirable compounds, such as ethyl phenols (4-ethylphenol and 4-ethylguaiacol), biogenic amines, and tartaric acid, and so on, as well as to facilitate some technological processes (i.e. in wine filtration to eliminate microorganisms). This review summarizes recent studies with applications of nanotechnology in viticulture in order to facilitate agronomic management and optimize grape production and in enology to improve wine quality and safety. © 2021 Society of Chemical Industry.

Influence of climate change and mechanized harvesting on maize (Zea mays L.) planting and northern limits in northeast China.

BACKGROUND: Northeast China is one of the most important maize producing areas in China. Due to limitations on crop growth resulting from temperature, whether this area can realize mechanical kernel harvesting maize (MKHM) will directly affect the stable development of maize in the region. The effects of climate change on the northern limits of early maturing MKHM were also analyzed in the study. RESULTS: The mean temperature during the maize growth period increased at a rate of 0.22 °C/10a from 1960 to 2018. The average growth periods for early, middle- and late-maturing common harvest maize (CHM) were 123, 135, and 140 days, respectively, and the accumulated temperature above 10 °C (AAT10) was 2400 °C, 2800 °C, and 3100 °C. The early maturing MKHM growth period was about 20 days longer than that of early maturing CHM, and thus the AAT10 of the MKHM was 2700 °C. From 2000-2018, the northern limits for the early maturing CHM maize planting were located from south of Nenjiang and Wudalianchi (47° 98' N-49° 74' N), while the northern limits for the early maturing MKHM maize were located in south Keshan, Nehe, and Hailun (46° 32' N-48° 70' N), which was about 148 km southward compared to the northern limits of the early maturing CHM maize. CONCLUSION: This study not only confirmed the northern limits of early maturing MKHM maize but also indicated that the development of MKHM offsets the influences of climate change on the northern limits of maize planting. This is very important for the sustainable development of maize in the region. © 2020 Society of Chemical Industry.

Saving time maintaining reliability: a new method for quantification of Tetranychus urticae damage in Arabidopsis whole rosettes.

BACKGROUND: The model species Tetranychus urticae produces important plant injury and economic losses in the field. The current accepted method for the quantification of the spider mite damage in Arabidopsis whole rosettes is time consuming and entails a bottleneck for large-scale studies such as mutant screening or quantitative genetic analyses. Here, we describe an improved version of the existing method by designing an automatic protocol. The accuracy, precision, reproducibility and concordance of the new enhanced approach are validated in two Arabidopsis accessions with opposite damage phenotypes. Results are compared to the currently available manual method. RESULTS: Image acquisition experiments revealed that the automatic settings plus 10 values of brightness and the black background are the optimal conditions for a specific recognition of spider mite damage by software programs. Among the different tested methods, the Ilastik-Fiji tandem based on machine learning was the best procedure able to quantify the damage maintaining the differential range of damage between accessions. In addition, the Ilastik-Fiji tandem method showed the lowest variability within a set of conditions and the highest stability under different lighting or background surroundings. Bland-Altman concordance results pointed out a negative value for Ilastik-Fiji, which implies a minor estimation of the damage when compared to the manual standard method. CONCLUSIONS: The novel approach using Ilastik and Fiji programs entails a great improvement for the quantification of the specific spider mite damage in Arabidopsis whole rosettes. The automation of the proposed method based on interactive machine learning eliminates the subjectivity and inter-rater-variability of the previous manual protocol. Besides, this method offers a robust tool for time saving and to avoid the damage overestimation observed with other methods.

Hydroprinted Liquid-Alloy-Based Morphing Electronics for Fast-Growing/Tender Plants: From Physiology Monitoring to Habit Manipulation.

Monitoring physiological signals and manipulating growth habits of living plants in real time are important for botany research, biohybrid plant robots, and precision agriculture. Although emerging epidermal electronics that can conveniently acquire vital signals of living organisms exhibit a high potential for such scenarios, it is a significant challenge to adapt such devices for plants, because they are fragile and usually have complex surfaces that can change significantly during rapid growth. A gentle fabrication process is critical in order to employ compliant electronic systems to adapt to this highly dynamic situation. In this study, a hydroprinted liquid-alloy-based morphing electronics (LAME) process is employed for fast-growing plants that will sense physiological signals and even function as a biohybrid to determine plant behavior on demand. Besides various surfaces of inorganic targeting substrates, pinning liquid alloy circuits onto the complex plant epidermis is enhanced by introducing high-surface-energy liquid. Functionally, the new developed LAME can be used to monitor leaf moisture content and length, and manipulate leaf and bean sprout orientation. This study lays the foundation for a new form of morphing electronics for botany or biohybrid plant robots, potentially impacting the next generation of precision agriculture and smart hybrid robots.

A Systematic Review of IoT Solutions for Smart Farming.

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

Precision Landing Test and Simulation of the Agricultural UAV on Apron.

Unmanned aerial vehicle (UAV) has been used to assist agricultural production. Precision landing control of UAV is critical for application of it in some specific areas such as greenhouses or livestock/poultry houses. For controlling UAV landing on a fixed or mobile apron/platform accurately, this study proposed an automatic method and tested it under three scenarios: (1) UAV landing at high operating altitude based on the GPS signal of the mobile apron; (2) UAV landing at low operating altitude based on the image recognition on the mobile apron; and (3) UAV landing progress control based on the fixed landing device and image detection to achieve a stable landing action. To verify the effectiveness of the proposed control method, apron at both stationary and mobile (e.g., 3 km/h moving speed) statuses were tested. Besides, a simulation was conducted for the UAV landing on a fixed apron by using a commercial poultry house as a model (135 L × 15 W × 3 H m). Results show that the average landing errors in high altitude and low altitude can be controlled within 6.78 cm and 13.29 cm, respectively. For the poultry house simulation, the landing errors were 6.22 ± 2.59 cm, 6.79 ± 3.26 cm, and 7.14 ± 2.41cm at the running speed of 2 km/h, 3 km/h, and 4 km/h, respectively. This study provides the basis for applying the UAV in agricultural facilities such as poultry or animal houses where requires a stricter landing control than open fields.

Monitoring Wheat Growth Using a Portable Three-Band Instrument for Crop Growth Monitoring and Diagnosis.

An instrument developed to monitor and diagnose crop growth can quickly and non-destructively obtain crop growth information, which is helpful for crop field production and management. Focusing on the problems with existing two-band instruments used for crop growth monitoring and diagnosis, such as insufficient information available on crop growth and low accuracy of some growth indices retrieval, our research team developed a portable three-band instrument for crop-growth monitoring and diagnosis (CGMD) that obtains a larger amount of information. Based on CGMD, this paper carried out studies on monitoring wheat growth indices. According to the acquired three-band reflectance spectra, the combined indices were constructed by combining different bands, two-band vegetation indices (NDVI, RVI, and DVI), and three-band vegetation indices (TVI-1 and TVI-2). The fitting results of the vegetation indices obtained by CGMD and the commercial instrument FieldSpec HandHeld2 was high and the new instrument could be used for monitoring the canopy vegetation indices. By fitting each vegetation index to the growth index, the results showed that the optimal vegetation indices corresponding to leaf area index (LAI), leaf dry weight (LDW), leaf nitrogen content (LNC), and leaf nitrogen accumulation (LNA) were TVI-2, TVI-1, NDVI (R730, R815), and NDVI (R730, R815), respectively. R2 values corresponding to LAI, LDW, LNC and LNA were 0.64, 0.84, 0.60, and 0.82, respectively, and their relative root mean square error (RRMSE) values were 0.29, 0.26, 0.17, and 0.30, respectively. The addition of the red spectral band to CGMD effectively improved the monitoring results of wheat LAI and LDW. Focusing the problem of vegetation index saturation, this paper proposed a method to construct the wheat-growth-index spectral monitoring models that were defined according to the growth periods. It improved the prediction accuracy of LAI, LDW, and LNA, with R2 values of 0.79, 0.85, and 0.85, respectively, and the RRMSE values of these growth indices were 0.22, 0.23, and 0.28, respectively. The method proposed here could be used for the guidance of wheat field cultivation.

Priorities for science to overcome hurdles thwarting the full promise of the 'digital agriculture' revolution.

The world needs to produce more food, more sustainably, on a planet with scarce resources and under changing climate. The advancement of technologies, computing power and analytics offers the possibility that 'digitalisation of agriculture' can provide new solutions to these complex challenges. The role of science is to evidence and support the design and use of digital technologies to realise these beneficial outcomes and avoid unintended consequences. This requires consideration of data governance design to enable the benefits of digital agriculture to be shared equitably and how digital agriculture could change agricultural business models; that is, farm structures, the value chain and stakeholder roles, networks and power relations, and governance. We argue that this requires transdisciplinary research (at pace), including explicit consideration of the aforementioned socio-ethical issues, data governance and business models, alongside addressing technical issues, as we now have to simultaneously deal with multiple interacting outcomes in complex technical, social, economic and governance systems. The exciting prospect is that digitalisation of science can enable this new, and more effective, way of working. The question then becomes: how can we effectively accelerate this shift to a new way of working in agricultural science? As well as identifying key research areas, we suggest organisational changes will be required: new research business models, agile project management; new skills and capabilities; and collaborations with new partners to develop 'technology ecosystems'. © 2018 The Authors. © 2018 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Easy-to-Use Sensors for Field Monitoring of Copper Contamination in Water and Pesticide-Sprayed Plants.

Copper (Cu2+)-containing pesticides are commonly used in agriculture to control fungal and bacterial diseases, but the release of large quantities of Cu2+ in water and soil can lead to harmful long-term consequences on the environment, organisms, and ecosystem health. Technology available to measure Cu2+ accumulation in the field is too expensive and complicated for general population use. We describe a low-cost sensor with simplified user operation for measuring Cu2+ content in environmental and agricultural samples at sensitivity levels comparable with a laboratory-based atomic absorption spectroscopy (AAS) method. The sensor is based on polyethyeleneimine (PEI), which has a strong chelating ability for Cu2+ ions. The PEI is stabilized on paper by layer-by-layer assembly with the PEI deposited sequentially within electrostatically charged poly(styrenesulfonate) (PSS). The PEI-PSS layers develop a vivid blue complex when interacting with Cu2+, and the resulting color intensity varies with the Cu2+ concentration. Our sensors give a yes or no response with the naked eye down to 10 µM when a preconcentration step was used. A more precise quantitative response can be obtained using a smartphone or scanner and free imaging software within a wide linear range from 10 to 2000 µM with a detection limit of 0.795 µM. The sensors were used for detecting commercial Cu2+-based pesticides in water and pesticide-sprayed plants within 15 min. Considering that these sensors are robust, simple to operate, and extremely stable, they could be ideal for remote monitoring of Cu2+ ion exposure and for the analysis of Cu2+ in environmental water and agricultural fields.

Local Structure Modulation Induced Highly Efficient Far-Red Luminescence of La1- xLu xAlO3:Mn4+ for Plant Cultivation.

Modulating the local environment around the emitting ions with component screening to increase the quantum yield and thermal stability is an effective and promising strategy for the design of high-performance fluorescence materials. In this work, smaller Lu3+ was introduced into the La3+ site in a Mn4+-activated LaAlO3 phosphor with the expectation of improving the luminescence properties via lattice contraction induced by cation substitution. Finally, a La1- xLu xAlO3:Mn4+ ( x = 0-0.04) perovskite phosphor with a high quantum yield of 86.0% and satisfactory thermal stability was achieved, and the emission peak at 729 nm well matches with the strongest absorption peak of the Phytochrome PFR. The favorable performances could be attributed to the suppressed cell volume and superior lattice rigidity after the substitution of Lu3+. This work not only obtains a highly efficient La1- xLu xAlO3:Mn4+ ( x = 0.02) phosphor, which holds great potential for application in plant-cultivation light-emitting diodes, but also provides an applicable strategy for further investigation of far-red-emitting phosphors.

Pesticide use and incident hyperthyroidism in farmers in the Agricultural Health Study.

BACKGROUND: Few studies have evaluated associations between pesticides and hyperthyroidism. OBJECTIVE: We evaluated associations between specific pesticides and incident hyperthyroidism in private pesticide applicators in the Agricultural Health Study. METHODS: We used Cox proportional hazards models to estimate HRs and 95% CIs for associations between pesticide use at enrolment and hyperthyroidism (n=271) in 35 150 applicators (mostly men), adjusting for potential confounders. RESULTS: Ever use of several pesticides (organophosphate insecticide malathion, fungicide maneb/mancozeb, herbicides dicamba, metolachlor, and atrazine in overall sample and chlorimuron ethyl among those ≤62 years) was associated with reduced hyperthyroidism risk, with HRs ranging from 0.50 (95% CI 0.30 to 0.83) for maneb/mancozeb to 0.77 (95% CI 0.59 to 1.00) for atrazine. Hyperthyroidism risk was lowest among those with higher intensity-weighted lifetime days of using carbofuran and chlorpyrifos (ptrend ≤0.05). CONCLUSIONS: Observed associations between pesticides and decreased risk of hyperthyroidism warrant further investigation.

Supplemental surveillance: a review of 2015 and 2016 agricultural injury data from news reports on AgInjuryNews.org.

BACKGROUND: Agriculture, forestry, fishing and hunting industry is the most hazardous occupational sector in the USA. Even with this level of occupational risk, several national and state-level occupational injury surveillance programmes have been eliminated, leaving regional efforts to analyse multiple sources and compile data on agricultural injuries and fatalities. No up-to-date centralised national database for agricultural injuries/fatalities in the USA currently exists. OBJECTIVE: Using the public data on AgInjuryNews.org, this study considered a wide range of variables to examine fatalities and injuries of the industry in 2015 and 2016. The results reported in this paper sought to explore and understand common data elements of US news reports. METHODS: As of 5 April 2017, more than 3000 articles across 36 years were contained in the dataset. We selected 2 years to review, 2015 and 2016, which represented the most complete years to date; 2015 was the first year in which systematic collection was initiated by the AgInjuryNews.org team. Data were coded based on the Occupational Injury and Illness Classification System source and event/exposure types. RESULTS: A total of 1345 victims were involved in 1044 incidents. Leading sources of injuries were vehicles and machinery, and the most common event/exposure type was transportation. CONCLUSIONS: This study demonstrated that data from AgInjuryNew.org is consistent with previous literature, and it can supply up-to-date data as an open-source surveillance supplement, disseminated for health and safety stakeholders.