Takehiro Furukawa, Fumihiro Yamane, Takuji Okumura
et al.
Insulin-like androgenic gland factor (IAG) is considered a key regulator of male sexual differentiation and maturation in decapod crustaceans. In several species, <i>IAG</i> expression is thought to be negatively regulated by the eyestalk, as demonstrated by eyestalk ablation (ESA) experiments. In the kuruma prawn <i>Marsupenaeus japonicus</i>, however, the upstream regulatory mechanisms of <i>IAG</i> (<i>Maj-IAG</i>) remain largely unclear. In the present study, males of different body sizes were subjected to ESA to elucidate these mechanisms. Bilateral ESA induced upregulation of <i>Maj-IAG</i> expression from day 7 onward, whereas unilateral ESA did not. Moreover, enhanced development of male reproductive organs and hypertrophy of the androgenic gland were observed from day 7 after bilateral ESA. These findings indicate that <i>Maj-IAG</i> is regulated by eyestalk-derived factor(s), supporting the presence of an eyestalk–androgenic gland endocrine axis in <i>M. japonicus</i>. By contrast, the expression of <i>Maj-Dsx2</i>, a homolog of doublesex (<i>Dsx</i>) that has recently been proposed as an upstream regulator of IAG, did not show a consistent increase following bilateral ESA across all experiments, suggesting that the involvement of <i>Maj-Dsx2</i> in this axis remains unclear. Overall, this study provides fundamental insights into the regulatory mechanisms of decapod male reproduction.
High-quality, naturally protected seeds prior to sowing, along with growth activation of seedlings, represent a promising approach to stabilising crop yield and quality. Enhancing plant resistance to dynamic environmental stresses, including harmful organisms, is one of the strategies for realising the biological potential of crop yields in breeding and seed production. This research aimed to experimentally evaluate a preparation based on humic substances, film formers, a nanocomposite, succinic acid, and microbiological carotene. Experiments were conducted using spring barley and wheat seeds. A seed encrustation technology employing a functional preparation was applied. Laboratory and field experiments were conducted at V. Dokuchaev Kharkiv National Agrarian University, Department of Plant Growing, over two years. The experimental design and economic efficiency assessment of the functional preparation in enhancing yield was carried out according to established methodologies. Pre-sowing seed treatment with the preparation resulted in improved field germination, synchronised seedling emergence, and increased yield. Comprehensive studies revealed that the preparation was compatible with fungicides, demonstrating a synergistic effect of their joint protective effect. Experimental results confirmed that seed incrustation with protective and stimulating formulations based on water-soluble polymers is an effective method for protecting plants from seed- and soil-borne infections while reducing the level of environmental pollution. The extended and enhanced fungicidal activity of film-forming protective and stimulating compositions was also demonstrated. Agricultural production tests indicated that the developed preparation was user-friendly, environmentally safe, and economically efficient, contributing to increased crop yields. The positive test results support practical recommendations for its application in both seed encrustation and grain crop spraying during the tillering and milky-wax ripeness phases
Marcos Abel Zuzuárregui, Mustafa Melih Toslak, Stefano Carpin
Artificial intelligence is transforming precision agriculture, offering farmers new tools to streamline their daily operations. While these technological advances promise increased efficiency, they often introduce additional complexity and steep learning curves that are particularly challenging for non-technical users who must balance tech adoption with existing workloads. In this paper, we present a natural language (NL) robotic mission planner that enables non-specialists to control heterogeneous robots through a common interface. By leveraging large language models (LLMs) and predefined primitives, our architecture seamlessly translates human language into intermediate descriptions that can be executed by different robotic platforms. With this system, users can formulate complex agricultural missions without writing any code. In the work presented in this paper, we extend our previous system tailored for wheeled robot mission planning through a new class of experiments involving robotic manipulation and computer vision tasks. Our results demonstrate that the architecture is both general enough to support a diverse set of robots and powerful enough to execute complex mission requests. This work represents a significant step toward making robotic automation in precision agriculture more accessible to non-technical users.
Lakshan Lavan, Lanojithan Thiyagarasa, Udara Muthugala
et al.
Conventional linear crop layouts, optimised for tractors, hinder robotic navigation with tight turns, long travel distances, and perceptual aliasing. We propose a robot-centric square spiral layout with a central tramline, enabling simpler motion and more efficient coverage. To exploit this geometry, we develop a navigation stack combining DH-ResNet18 waypoint regression, pixel-to-odometry mapping, A* planning, and model predictive control (MPC). In simulations, the spiral layout yields up to 28% shorter paths and about 25% faster execution for waypoint-based tasks across 500 waypoints than linear layouts, while full-field coverage performance is comparable to an optimised linear U-turn strategy. Multi-robot studies demonstrate efficient coordination on the spirals rule-constrained graph, with a greedy allocator achieving 33-37% lower batch completion times than a Hungarian assignment under our setup. These results highlight the potential of redesigning field geometry to better suit autonomous agriculture.
Osama Zafar, Rosemarie Santa González, Alfonso Morales
et al.
Digital agriculture is transforming the way we grow food by utilizing technology to make farming more efficient, sustainable, and productive. This modern approach to agriculture generates a wealth of valuable data that could help address global food challenges, but farmers are hesitant to share it due to privacy concerns. This limits the extent to which researchers can learn from this data to inform improvements in farming. This paper presents the Digital Agriculture Sandbox, a secure online platform that solves this problem. The platform enables farmers (with limited technical resources) and researchers to collaborate on analyzing farm data without exposing private information. We employ specialized techniques such as federated learning, differential privacy, and data analysis methods to safeguard the data while maintaining its utility for research purposes. The system enables farmers to identify similar farmers in a simplified manner without needing extensive technical knowledge or access to computational resources. Similarly, it enables researchers to learn from the data and build helpful tools without the sensitive information ever leaving the farmer's system. This creates a safe space where farmers feel comfortable sharing data, allowing researchers to make important discoveries. Our platform helps bridge the gap between maintaining farm data privacy and utilizing that data to address critical food and farming challenges worldwide.
Drones are promising for data collection in precision agriculture, however, they are limited by their battery capacity. Efficient path planners are therefore required. This paper presents a drone path planner trained using Reinforcement Learning (RL) on an abstract simulation that uses object detections and uncertain prior knowledge. The RL agent controls the flight direction and can terminate the flight. By using the agent in combination with the drone's flight controller and a detection network to process camera images, it is possible to evaluate the performance of the agent on real-world data. In simulation, the agent yielded on average a 78% shorter flight path compared to a full coverage planner, at the cost of a 14% lower recall. On real-world data, the agent showed a 72% shorter flight path compared to a full coverage planner, however, at the cost of a 25% lower recall. The lower performance on real-world data was attributed to the real-world object distribution and the lower accuracy of prior knowledge, and shows potential for improvement. Overall, we concluded that for applications where it is not crucial to find all objects, such as weed detection, the learned-based path planner is suitable and efficient.
Malakhi Hopkins, Alice Kate Li, Shobhita Kramadhati
et al.
Common remote sensing modalities (RGB, multispectral, hyperspectral imaging or LiDAR) are often used to indirectly measure crop health and do not directly capture plant stress indicators. Commercially available direct leaf sensors are bulky, powered electronics that are expensive and interfere with crop growth. In contrast, low-cost, passive and bio-degradable leaf sensors offer an opportunity to advance real-time monitoring as they directly interface with the crop surface while not interfering with crop growth. To this end, we co-design a sensor-detector system, where the sensor is a passive colorimetric leaf sensor that directly measures crop health in a precision agriculture setting, and the detector autonomously obtains optical signals from these leaf sensors. The detector comprises a low size weight and power (SWaP) mobile ground robot with an onboard monocular RGB camera and object detector to localize each leaf sensor, as well as a hyperspectral camera with a motorized mirror and halogen light to acquire hyperspectral images. The sensor's crop health-dependent optical signals can be extracted from the hyperspectral images. The proof-of-concept system is demonstrated in row-crop environments both indoors and outdoors where it is able to autonomously navigate, locate and obtain a hyperspectral image of all leaf sensors present, and acquire interpretable spectral resonance with 80 $\%$ accuracy within a required retrieval distance from the sensor.
Agricultural field operations are generally tracked as work records (WR), incorporating data points such as; work type, machine type, timestamped trajectories and field information. WR data which is automatically recorded by modern machinery equipped with Information and Communication Technologies (ICT) can enable efficient farm management decision making. Globally, farmers often rely on aged or legacy farming machinery and manual data recording, which introduces significant labor costs and increases the risk of inaccurate data input. To address this challenge, a field study in Central Japan was conducted to showcase automated data collection by retrofitting legacy farming machinery with low-cost Internet of Things (IoT) devices. For single-purpose vehicles (SPV), which only carry out single work types such as planting, LTE (Long Term Evolution) and Global Navigation Satellite System (GNSS) units were installed to record trajectory data. For multi-purpose vehicles (MPV), such as tractors which perform multiple work types, the configuration settings of these vehicles had to include implements and attachments data. To obtain this data, industry standard LTE-GNSS Bluetooth gateways were fitted onto MPV and low-cost BLE (Bluetooth Low Energy) beacons were attached to implements. After installation, over a seven-month field preparation and planting period 1,623 WR, including 421 WR for SPV and 1,120 WR for MVP, were automatically obtained. For MPV, the WR included detailed configuration settings enabling detection of the specific work types. These findings demonstrate the potential of low cost IoT GNSS devices for precision agriculture strategies to support management decisions in farming operations.
Pavel Pereslavtsev, Christian Bachmann, Joelle Elbez-Uzan
et al.
There is widespread use of nuclear radiation for medical imagery and treatments. Worldwide, almost 40 million treatments are performed per year. There are also applications of radiation sources in other commercial fields, e.g., for weld inspection or steelmaking processes, in consumer products, in the food industry, and in agriculture. The large number of neutrons generated in a fusion reactor such as DEMO could potentially contribute to the production of the required radioactive isotopes. The associated commercial value of these isotopes could mitigate the capital investments and operating costs of a large fusion plant. The potential of producing various radioactive isotopes was studied from material pieces arranged inside a DEMO equatorial port plug. In this location, they are exposed to an intensive neutron spectrum suitable for a high isotope production rate. For this purpose, the full 3D geometry of one DEMO toroidal sector with an irradiation chamber in the equatorial port plug was modeled with an MCNP code to perform neutron transport simulations. Subsequent activation calculations provide detailed information on the quality and composition of the produced radioactive isotopes. The technical feasibility and the commercial potential of the production of various isotopes in the DEMO port are reported.
Ali Qorbani, Mohammad Khalili, Saeidreza Nourollahifard
et al.
Abstract Rickettsia occurs worldwide and rickettsiosis is recognized as an emerging infection in several parts of the world. Ticks are reservoir hosts for pathogenic Rickettsia species in humans and domestic animals. Most pathogenic Rickettsia species belong to the spotted Fever Group (SFG). This study aimed to identify and diagnose tick fauna and investigate the prevalence of Rickettsia spp. in ticks collected from domestic animals and dogs in the rural regions of Kerman Province, Southeast Iran. In this study, tick species (fauna) were identified and 2100 ticks (350 pooled samples) from two genera and species including Rhipicephalus linnaei (1128) and Hyalomma deteritum (972) were tested to detect Rickettsia genus using Real-time PCR. The presence of the Rickettsia genus was observed in 24.9% (95%CI 20.28–29.52) of the pooled samples. Sequencing and phylogenetic analyses revealed the presence of Rickettsia aeschlimannii (48.98%), Rickettsia conorii israelensis (28.57%), Rickettsia sibirica (20.41%), and Rickettsia helvetica (2.04%) in the positive samples. The results showed a significant association between county variables and the following variables: tick spp. (p < 0.001), Rickettsia genus infection in ticks (p < 0.001) and Rickettsia spp. (p < 0.001). In addition, there was a significant association between tick species and host animals (dogs and domestic animals) (p < 0.001), Rickettsia spp infection in ticks (p < 0.001), and Rickettsia spp. (p < 0.001). This study indicates a high prevalence of Rickettsia spp. (SFG) in ticks of domestic animals and dogs in rural areas of Kerman Province. The health system should be informed of the possibility of rickettsiosis and the circulating species of Rickettsia in these areas.
Darko Uher, Sanja Fabek Uher, Nevena Opačić
et al.
Cultivated nettle Urtica dioica L., a member of the Urticaceae family, is widely distributed throughout the temperate regions of the world and can be used as a nutritious feed for animals through the winter period. The aim of this research was to determine (i) the nutritional value of freshly cultivated nettle Urtica dioica L. grown in the open field and (ii) the fermentation value of cultivated nettle Urtica dioica L. ensiled with additives, including a commercial inoculant containing bacteria that produce lactic acid and molasses from sugar beet after 60 days of ensiling. Cultivated nettle Urtica dioica L. was ensiled in six treatments: without additions (control); with sugar beet molasses (2, 4, and 6%) per 1 kg of fresh mass and a commercial inoculant (2 and 4 g/t of fresh mass) in five replicates. The results of this research showed that the cultivated nettle Urtica dioica L. contains a large proportion of proteins and some essential minerals, including calcium, and is especially rich in magnesium and iron. Without the addition of sugar beet molasses and without treatment with a commercial inoculant, poorly preserved silage from cultivated nettle was obtained. With 2% molasses, poorly preserved silage was obtained, but with 4 and 6% molasses, well-preserved silage from cultivated nettle was obtained. Based on the results of these studies, it is recommended to use sugar beet molasses during ensiling in a concentration of 4 to 6% of the fresh mass of cultivated nettle. In future research, it would be interesting to test the joint application of commercial inoculants and sugar beet molasses when preparing cultivated nettle Urtica dioica L. for silage on the farm.
Catarina Leal, Ales Eichmeier, Kateřina Štůsková
et al.
With a reduction in available chemical treatments, there is an increased interest in biological control of grapevine trunk diseases. Few studies have investigated the impact of introducing beneficial microorganisms in the rhizosphere on the existing indigenous soil microbiome. In this study, we explored the effect of two biocontrol agents (BCAs), Trichoderma atroviride SC1 (Ta SC1) (Vintec; Certis Belchim) and Bacillus subtilis PTA-271 (Bs PTA-271), on the grapevine rhizosphere bacterial and fungal microbiome as well as plant defense expression using high-throughput amplicon sequencing and quantitative real-time polymerase chain reaction (PCR), respectively. Additionally, we quantified both Ta SC1 and Bs PTA-271 in the rhizosphere over time using droplet digital PCR. The fungal microbiome was more affected by factors such as soil type, BCA treatment, and sampling time compared with the bacterial microbiome. Specifically, Ta SC1 application produced negative impacts on fungal diversity, whereas application of BCAs did not affect bacterial diversity. Interestingly, the survival and establishment of both BCAs showed opposite trends depending on the soil type, indicating that the physicochemical properties of soils have a role in BCA establishment. Fungal co-occurrence networks were less complex than bacterial networks but highly impacted by Ta SC1 application. Soils treated with Ta SC1 presented more complex and stable co-occurrence networks, with a higher number of positive correlations. Induced grapevine defenses also differed according to the soil, being more affected by BCA inoculation on sandy soil. The findings of this research emphasize the complex relationships among microorganisms in the rhizosphere and highlight the significance of taking into account various factors, such as soil type, sampling time, and BCA treatment, and their influence on the structure and dynamics of microbial communities.
Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only ``normal" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating ``normal" images for training. Evaluation on the Agriculture-Vision data challenge shows a mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-Vision Challenge Dataset
Silvia Melissa Manrique-Veja, Oscar Alvarado-Sanabria
Abstract This study aims at assessing the impact of compost application on the physical (porosity, volumetric-moisture and bulk density) and the chemical traits of soil (pH, organic carbon, electrical conductivity, cation exchange capacity and soil nutrients) on the leaf nutrient concentration and growth (height, diameter, new leaf-structures and chlorophyll content) of five native species used in urban forestry. Using a two-way factorial design, we evaluated three substrates: i) Soil (ii) Soil-compost mixture SC-12.5 (12.5 % compost (v/v)) (iii) Soil-compost mixture SC-25 (25 % compost (v/v)) and five species: Retrophyllum rospigliosii, Inga edulis, Citharexylum montanum, Caesalpinia spinosa, and Citharexylum sulcatum. We found that SC-25 and SC-12.5 increased the electric conductivity, cation exchange capacity, organic carbon, and soil base saturation. Moreover, compost addition increased the growth of the five native species evaluated. Such results suggest that compost-application is a viable option to improve soil fertility and promote the growth of native trees.
This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making informed decisions considering factors such as installation budget, government incentives, energy requirements, system cost, and long-term benefits. By implementing a reward mechanism, the DQN learns to make data-driven decisions on PV integration. The analysis provides a comprehensive understanding of how DQNs can support investors in making decisions about PV installations in agriculture. This research has significant implications for promoting sustainable and efficient farming practices while also paving the way for future advancements in this field. By leveraging DQNs, agricultural investors can make optimized decisions that improve energy efficiency, reduce environmental impact, and enhance profitability. This study contributes to the advancement of PV integration in agriculture and encourages further innovation in this promising area.
A key challenge for much of the machine learning work on remote sensing and earth observation data is the difficulty in acquiring large amounts of accurately labeled data. This is particularly true for semantic segmentation tasks, which are much less common in the remote sensing domain because of the incredible difficulty in collecting precise, accurate, pixel-level annotations at scale. Recent efforts have addressed these challenges both through the creation of supervised datasets as well as the application of self-supervised methods. We continue these efforts on both fronts. First, we generate and release an improved version of the Agriculture-Vision dataset (Chiu et al., 2020b) to include raw, full-field imagery for greater experimental flexibility. Second, we extend this dataset with the release of 3600 large, high-resolution (10cm/pixel), full-field, red-green-blue and near-infrared images for pre-training. Third, we incorporate the Pixel-to-Propagation Module Xie et al. (2021b) originally built on the SimCLR framework into the framework of MoCo-V2 Chen et al.(2020b). Finally, we demonstrate the usefulness of this data by benchmarking different contrastive learning approaches on both downstream classification and semantic segmentation tasks. We explore both CNN and Swin Transformer Liu et al. (2021a) architectures within different frameworks based on MoCo-V2. Together, these approaches enable us to better detect key agricultural patterns of interest across a field from aerial imagery so that farmers may be alerted to problematic areas in a timely fashion to inform their management decisions. Furthermore, the release of these datasets will support numerous avenues of research for computer vision in remote sensing for agriculture.