Precision agriculture promises higher yields and sustainability, but adoption is slowed by the high cost of cyber-physical systems (CPS) and the lack of systematic design methods. We present a cost-aware design space exploration (DSE) framework for multimodal drone-rover platforms to integrate budget, energy, sensing, payload, computation, and communication constraints. Using integer linear programming (ILP) with SAT-based verification, our approach trades off among cost, coverage, and payload while ensuring constraint compliance and a multitude of alternatives. We conduct case studies on smaller and larger-sized farms to show that our method consistently achieves full coverage within budget while maximizing payload efficiency, outperforming state-of-the-art CPS DSE approaches.
This study addresses the demand for real-time detection of tomatoes and tomato flowers by agricultural robots deployed on edge devices in greenhouse environments. Under practical imaging conditions, object detection systems often face challenges such as large scale variations caused by varying camera distances, severe occlusion from plant structures, and highly imbalanced class distributions. These factors make conventional object detection approaches that rely on fully annotated datasets difficult to simultaneously achieve high detection accuracy and deployment efficiency. To overcome these limitations, this research proposes an active learning driven lightweight object detection framework, integrating data analysis, model design, and training strategy. First, the size distribution of objects in raw agricultural images is analyzed to redefine an operational target range, thereby improving learning stability under real-world conditions. Second, an efficient feature extraction module is incorporated to reduce computational cost, while a lightweight attention mechanism is introduced to enhance feature representation under multi-scale and occluded scenarios. Finally, an active learning strategy is employed to iteratively select high-information samples for annotation and training under a limited labeling budget, effectively improving the recognition performance of minority and small-object categories. Experimental results demonstrate that, while maintaining a low parameter count and inference cost suitable for edge-device deployment, the proposed method effectively improves the detection performance of tomatoes and tomato flowers in raw images. Under limited annotation conditions, the framework achieves an overall detection accuracy of 67.8% mAP, validating its practicality and feasibility for intelligent agricultural applications.
Morchella rotation and intercropping is a new and efficient ecological planting mode, which not only contributes to economic growth, but also promotes the sustainable development of agriculture and has high ecological benefits. Morchella sextelata is an edible mushroom that relies on soil-based cultivation. Understanding the composition and dynamics of soil fungal communities under different cropping systems is crucial for optimising its cultivation. This study investigated the fungal community characteristics of Morchella spp. under different rotation and intercropping patterns, together with the associated environmental factors. Using Illumina NovaSeq high-throughput sequencing coupled with ecological and statistical analyses, the relative abundance, alpha diversity index, beta diversity, and intergroup differences in fungal communities were assessed. Additionally, key soil physical and chemical properties were evaluated across four cultivation systems: conventional Morchella spp. cultivation, Morchella sextelata—ginger rotation, vine—Morchella sextelata intercropping, and mulberry tree—Morchella sextelata intercropping. Our results indicate that Morchella spp. cultivation leads to a significant decline in soil fungal diversity compared to uncultivated soils This indicates that cultivation with Morchella spp. simplifies the soil fungal community structure to some extent. Furthermore, distinct variations in fungal community structure were observed across the different cropping systems. Regarding major pathomycete, the relative abundance of Paecilomyces penicillatus increases in vine intercropping soil (VIS), whereas Botryotrichum atrogriseum and Paecilomyces sp. are more abundant in ginger rotation soil (GRS). Similarly, Fusarium solani and Mortierella sp. exhibit higher relative abundance in mulberry tree intercropping soil (MTIS) and fallow soil (FS) compared to natural soil (NS). Functional prediction analysis indicated a general increase in the relative abundance of potential animal and plant pathogenic fungi across all the soil samples, excluding the VIS. This increase was most pronounced in GRS. Further study revealed that the physical and chemical properties of covering soil, including pH, available potassium (AK), available phosphorus (AP), and total phosphorus (TP), significantly influence fungal community diversity and structure. A significant negative correlation was observed between pH and the relative abundance of Fusarium fungi. These findings provide valuable data for further exploration of the ecological mechanisms underlying Morchella spp. cultivation, including rotation constraints and disease dynamics. Ultimately, this research aims to support the development of sustainable and high-quality Morchella spp. production.
Precision agriculture (PA) plays a crucial role in minimizing the utilization of resources to increase crop yield and reduce environmental impact while enhancing sustainable agriculture (SA). In Tanzania, agriculture contributes about 28% to the gross domestic product (GDP), and more than 65% of the population depends on it. Limited information on the use of PA technologies and their potential impact in enhancing SA is available in Tanzania. The aim of this review is to discuss the agricultural sector in Tanzania and the role of PA technologies in promoting sustainable farming practices. Challenges for the adoption of PA technologies and strategies to promote their adoption are also discussed. In Tanzania, few PA technologies including sensors, smart irrigation technologies, and mobile‐based PA technologies are currently used in agriculture sectors. However, the country has the potential for adopting PA technologies that can enhance data‐driven decision‐making. The importance of continuous research and innovation in adapting PA technologies to the local Tanzanian context is addressed in this review. This study provides vital information on the current status of PA application and potential impacts of PA technologies (such as reduced input costs and increased yield) to enhance agricultural sustainability in Tanzania as well as worldwide.
Weeds significantly reduce crop yields worldwide and pose major challenges to sustainable agriculture. Traditional weed management methods, primarily relying on chemical herbicides, risk environmental contamination and lead to the emergence of herbicide-resistant species. Precision weeding, leveraging computer vision and machine learning methods, offers a promising eco-friendly alternative but is often limited by reliance on high-power computational platforms. This work presents an optimized, low-power edge AI system for weeds detection based on the YOLOv8n object detector deployed on the STM32U575ZI microcontroller. Several compression techniques are applied to the detection model, including structured pruning, integer quantization and input image resolution scaling in order to meet strict hardware constraints. The model is trained and evaluated on the CropAndWeed dataset with 74 plant species, achieving a balanced trade-off between detection accuracy and efficiency. Our system supports real-time, in-situ weeds detection with a minimal energy consumption of 51.8mJ per inference, enabling scalable deployment in power-constrained agricultural environments.
Mark Freyhof, George Grispos, Santosh K. Pitla
et al.
As various technologies are integrated and implemented into the food and agricultural industry, it is increasingly important for stakeholders throughout the sector to identify and reduce cybersecurity vulnerabilities and risks associated with these technologies. However, numerous industry and government reports suggest that many farmers and agricultural equipment manufacturers do not fully understand the cyber threats posed by modern agricultural technologies, including CAN bus-driven farming equipment. This paper addresses this knowledge gap by attempting to quantify the cybersecurity risks associated with cyberattacks on farming equipment that utilize CAN bus technology. The contribution of this paper is twofold. First, it presents a hypothetical case study, using real-world data, to illustrate the specific and wider impacts of a cyberattack on a CAN bus-driven fertilizer applicator employed in row-crop farming. Second, it establishes a foundation for future research on quantifying cybersecurity risks related to agricultural machinery.
India is an agro-based economy and proper information about agricultural practices is the key to optimal agricultural growth and output. In order to answer the queries of the farmer, we have build an agricultural chatbot based on the dataset from Kisan Call Center. This system is robust enough to answer queries related to weather, market rates, plant protection and government schemes. This system is available 24* 7, can be accessed through any electronic device and the information is delivered with the ease of understanding. The system is based on a sentence embedding model which gives an accuracy of 56%. After eliminating synonyms and incorporating entity extraction, the accuracy jumps to 86%. With such a system, farmers can progress towards easier information about farming related practices and hence a better agricultural output. The job of the Call Center workforce would be made easier and the hard work of various such workers can be redirected to a better goal.
Taminul Islam, Toqi Tahamid Sarker, Khaled R Ahmed
et al.
Weed management remains a critical challenge in agriculture, where weeds compete with crops for essential resources, leading to significant yield losses. Accurate detection of weeds at various growth stages is crucial for effective management yet challenging for farmers, as it requires identifying different species at multiple growth phases. This research addresses these challenges by utilizing advanced object detection models, specifically, the Detection Transformer (DETR) with a ResNet50 backbone and RetinaNet with a ResNeXt101 backbone, to identify and classify 16 weed species of economic concern across 174 classes, spanning their 11 weeks growth stages from seedling to maturity. A robust dataset comprising 203,567 images was developed, meticulously labeled by species and growth stage. The models were rigorously trained and evaluated, with RetinaNet demonstrating superior performance, achieving a mean Average Precision (mAP) of 0.907 on the training set and 0.904 on the test set, compared to DETR's mAP of 0.854 and 0.840, respectively. RetinaNet also outperformed DETR in recall and inference speed of 7.28 FPS, making it more suitable for real time applications. Both models showed improved accuracy as plants matured. This research provides crucial insights for developing precise, sustainable, and automated weed management strategies, paving the way for real time species specific detection systems and advancing AI-assisted agriculture through continued innovation in model development and early detection accuracy.
C.L. van Zyl, H.K. Eriksson, E.A.M. Bokkers
et al.
ABSTRACT: In cow-calf contact (CCC) systems breaking the maternal bond may induce stress for the cow, thereby affecting feed intake, milk yield, milk flow rate, and milk electrical conductivity. This study aimed to determine the consequences of weaning and separation strategies in CCC systems for feed intake and milking characteristics of the cow. In 2 experiments, Swedish Holstein and Swedish Red cows either had (experiment 1) whole-day CCC (CCC1, n = 12) for 8.5 ± 1.2 wk (mean ± SD) followed by 12 h of daytime CCC for 8 wk, before abrupt weaning and separation at 16.4 ± 1.2 wk, or (experiment 2) whole-day CCC for 16 ± 1.0 wk; thereafter half of the calves were weaned via nose flaps for 2 wk (NF, n = 10) before physical separation and half via nose flaps for 1 wk and fence-line contact for 1 wk (NFFL, n = 9). Cows were compared with conventionally managed cows (CONV1 or CONV2 in experiment 1 or 2) separated from their calves within 12 h postpartum. In experiment 1, the study period included the week before and after the system switch from whole-day to daytime CCC, and the week before and after separation. In experiment 2, the study period included the week before the start of weaning, during weaning, and 1 week after separation. All cows were milked in the same automatic milking unit. In experiment 1, feed intake of CCC1 cows at separation tended to be lower than CONV1 cows. In experiment 2, roughage intake of NF, NFFL, and CONV2 cows did not differ, but the concentrate intake of NF cows was lower than that of CONV2 cows. In experiment 1, the system switch did not affect milking characteristics. However, after separation, machine milk yield and milk electrical conductivity of CCC1 cows increased, remaining lower than CONV1 cows. In experiment 2, machine milk yield of NF and NFFL cows increased when calves were fitted with nose flaps, but remained lower than CONV2 cows. In the week after separation, milk yield of NFFL cows was similar to that of CONV2 cows, and the NF cows remained lower. In the week before weaning, milk flow rates of NF cows were lower than those of CONV2 cows, and the NFFL cows did not differ. Before weaning, milk electrical conductivity of NF and NFFL cows was lower than that of CONV2 cows, but not thereafter. In conclusion, machine milk yield of CCC cows remained lower either until the week of separation, for NFFL cows, or until 3 or 11 wk after weaning and separation for CCC1 and NF cows of experiments 1 and 2, respectively. Cow-calf contact reduced milk electrical conductivity, and milk and peak milk flow rates increased the week after separation of cow and calf. Not for experiment 2, but for experiment 1, cow roughage and concentrate intake decreased at separation and recovered within a week, indicating that abrupt separation exerted a greater impact on the cow than separation after nose flap weaning or fence-line contact. Future studies should compare both weaning strategies within the same experimental setup, also focusing on the consequences for calves.
Alicia Manzanares-Pedrosa, Joanna Szumilas, Teresa Ayats
et al.
Thermophilic Campylobacter spp. are the main cause of gastrointestinal illness in humans through contaminated food. Poultry and poultry products are the main sources of Campylobacter infection. Epidemiological data on Campylobacter prevalence and load in broiler livers remain limited and its presence in this offal may be associated with the caecal load. Hence, this study aimed to determine the prevalence and levels of Campylobacter in chicken livers, both from the surface and inner tissue, compared with that of caeca, by sampling 56 flocks from two slaughterhouses in Spain. Three carcasses per flock were randomly collected during evisceration (n = 168 livers and caecal contents). Overall Campylobacter prevalence was 57.1 % in caecal samples, 77.9 % in surface liver samples and 35.7 % in the inner tissue liver. C. jejuni was the most common species in all sample types and coinfections with C. coli were more prevalent in livers than in the caeca samples. However, there was no relationship between Campylobacter species (C. jejuni, C. coli) and sample type (P > 0.05). The data highlights the role of chicken offal as a potential source of human campylobacteriosis, particularly because of the high Campylobacter load (>103 CFU/liver) in a high proportion of the surface liver samples (40.1 %). However, this high load was only detected in 6.6 % of the inner tissue livers. Restriction fragment length polymorphism (RFLP) analysis revealed a high genetic diversity with 107 different profiles among 473 genotyped Campylobacter isolates. Translocation of Campylobacter strains was demonstrated, with the same RFLP profile identified in isolates from the caeca and the inner liver tissue of the same carcass (14.9 %). Cross-contamination was also revealed, since the same RFLP profile was identified in isolates from the caeca and the surface of the liver from the same carcass (11.9 %). Targeted measures on broiler farms and slaughterhouses to reduce Campylobacter prevalence and cross-contamination in chicken offal will help to reduce the risk of campylobacteriosis for consumers.
This research conducts an analysis of the sustainability of urban agriculture in Shanghai over the period 2010 to 2020, employing the Triple Bottom Line (TBL) concept as a framework to evaluate sustainability across economic, environmental, and social dimensions through the formulation and application of a comprehensive indicator system. Utilizing the Delphi method alongside the Analytic Hierarchy Process (AHP) for determining indicators and their respective weights, this study adopts a methodologically rigorous approach to analysis. The findings reveal an overall enhancement in agricultural sustainability, albeit accompanied by a decline in economic sustainability. Notably, environmental sustainability emerged as a paramount concern, underscoring the essentiality of incorporating environmental indicators within urban agricultural initiatives. The paper addresses significant challenges such as elevated land prices, demographic shifts, and the imperative for more stringent environmental regulations. It advocates for a multidimensional strategy integrating advanced agricultural technologies and cross-sectoral partnerships to bolster sustainability. Furthermore, the study accentuates the necessity of achieving equilibrium among economic feasibility, environmental stewardship, and social equity to pursue sustainable urban agriculture in Shanghai. Additionally, it highlights the critical role of strategic agricultural policy formulation in fostering sectoral resilience and ensuring enduring sustainability.
Tianyi Zhang, Joshua Ofori Boateng, Taimoor UI Islam
et al.
As wireless networks evolve towards open architectures like O-RAN, testing, and integration platforms are crucial to address challenges like interoperability. This paper describes ARA-O-RAN, a novel O-RAN testbed established through the NSF Platforms for Advanced Wireless Research (PAWR) ARA platform. ARA provides an at-scale rural wireless living lab focused on technologies for digital agriculture and rural communities. As an O-RAN Alliance certified Open Testing and Integration Centre (OTIC), ARA launched ARA-O-RAN -- the first public O-RAN testbed tailored to rural and agriculture use cases, together with the end-to-end, whole-stack programmability. ARA-O-RAN uniquely combines support for outdoor testing across a university campus, surrounding farmlands, and rural communities with a 50-node indoor sandbox. The testbed facilitates vital R\&D to implement open architectures that can meet rural connectivity needs. The paper outlines ARA-O-RAN's hardware system design, software architecture, and enabled research experiments. It also discusses plans aligned with national spectrum policy and rural spectrum innovation. ARA-O-RAN exemplifies the value of purpose-built wireless testbeds in accelerating impactful wireless research.
Javier Penuela, Cecile Ben, Stepan Boldyrev
et al.
Demand response (DR) programs currently cover about 2\% of the average annual global demand, which is far from contributing to the International Energy Agency's ``Net Zero by 2050'' roadmap's 20\% target. While aggregation of many small flexible loads such as individual households can help reaching this target, increasing the participation of industries that are major electricity consumers is certainly a way forward. The indoor agriculture sector currently experiences a significant growth to partake in the sustainable production of high-quality food world-wide. As energy-related costs, up to 40\% of the total expenses, may preclude full maturity of this industry, DR participation can result in a win-win situation. Indeed, the agriculture system must transform and become a sustainable source of food for an increasing number of people worldwide under the constraints of preservation of soils and water, carbon footprint, and energy efficiency. We considered the case of the Russian Federation where indoor farming is burgeoning and already represents a load of several thousand megawatts. To show the viability of the indoor farming industry participation in implicit and explicit DR programs, we built a physical model of a vertical farm inside a phytotron with complete control of environmental parameters including ambient temperature, relative humidity, CO$_2$ concentration, and photosynthetic photon flux density. This phytotron was used as a model greenhouse. We grew different varieties of leafy plants under simulated DR conditions and control conditions on the same setup. Our results show that the indoor farming dedicated to greens can participate in DR without adversely affecting plant production and that this presents an economic advantage.
Muhammad Arbab Arshad, Talukder Zaki Jubery, Tirtho Roy
et al.
As Vision Language Models (VLMs) become increasingly accessible to farmers and agricultural experts, there is a growing need to evaluate their potential in specialized tasks. We present AgEval, a comprehensive benchmark for assessing VLMs' capabilities in plant stress phenotyping, offering a solution to the challenge of limited annotated data in agriculture. Our study explores how general-purpose VLMs can be leveraged for domain-specific tasks with only a few annotated examples, providing insights into their behavior and adaptability. AgEval encompasses 12 diverse plant stress phenotyping tasks, evaluating zero-shot and few-shot in-context learning performance of state-of-the-art models including Claude, GPT, Gemini, and LLaVA. Our results demonstrate VLMs' rapid adaptability to specialized tasks, with the best-performing model showing an increase in F1 scores from 46.24% to 73.37% in 8-shot identification. To quantify performance disparities across classes, we introduce metrics such as the coefficient of variation (CV), revealing that VLMs' training impacts classes differently, with CV ranging from 26.02% to 58.03%. We also find that strategic example selection enhances model reliability, with exact category examples improving F1 scores by 15.38% on average. AgEval establishes a framework for assessing VLMs in agricultural applications, offering valuable benchmarks for future evaluations. Our findings suggest that VLMs, with minimal few-shot examples, show promise as a viable alternative to traditional specialized models in plant stress phenotyping, while also highlighting areas for further refinement. Results and benchmark details are available at: https://github.com/arbab-ml/AgEval
Manuel Knott, Divinefavour Odion, Sameer Sontakke
et al.
Visual inspection for defect grading in agricultural supply chains is crucial but traditionally labor-intensive and error-prone. Automated computer vision methods typically require extensively annotated datasets, which are often unavailable in decentralized supply chains. We address this challenge by evaluating the Segment Anything Model (SAM) to generate dense panoptic segmentation masks from sparse annotations. These dense predictions are then used to train a supervised panoptic segmentation model. Focusing on banana surface defects (bruises and scars), we validate our approach using 476 field images annotated with 1440 defects. While SAM-generated masks generally align with human annotations, substantially reducing annotation effort, we explicitly identify failure cases associated with specific defect sizes and shapes. Despite these limitations, our approach offers practical estimates of defect number and relative size from panoptic masks, underscoring the potential and current boundaries of foundation models for defect quantification in low-data agricultural scenarios. GitHub: https://github.com/manuelknott/banana-defect-segmentation
Tom Baby, Mahendra Kumar Gohil, Bishakh Bhattacharya
In the futuristic agricultural fields compatible with Agriculture 4.0, robots are envisaged to navigate through crops to perform functions like pesticide spraying and fruit harvesting, which are complex tasks due to factors such as non-geometric internal obstacles, space constraints, and outdoor conditions. In this paper, we attempt to employ Deep Reinforcement Learning (DRL) to solve the problem of 4WIS4WID mobile robot navigation in a structured, automated agricultural field. This paper consists of three sections: parameterization of four-wheel steering configurations, crop row tracking using DRL, and autonomous navigation of 4WIS4WID mobile robot using DRL through multiple crop rows. We show how to parametrize various configurations of four-wheel steering to two variables. This includes symmetric four-wheel steering, zero-turn, and an additional steering configuration that allows the 4WIS4WID mobile robot to move laterally. Using DRL, we also followed an irregularly shaped crop row with symmetric four-wheel steering. In the multiple crop row simulation environment, with the help of waypoints, we effectively performed point-to-point navigation. Finally, a comparative analysis of various DRL algorithms that use continuous actions was carried out.
Optimum condition at 64.80% maize flour, 20% groundnut paste and 13.20 % palm oil was formulated to produced nutritionally enhanced aadun snack. The snack was stored in the different storage materials namely, sweet prayer plant leaves (control) which is usually used by most locals, low density polyethylene (LDPE), high density polyethylene (HDPE) and food grade plastic container (PC). The initial properties (energy, oxidative and sensory properties) of the enhanced aadun (before storage) were investigated and stored in each of the storage materials. The enhanced aadun samples in each storage material were analysed at two weeks interval for eighteen weeks. The results obtained were analysed statistically to examine the effect of the storage material on the aforementioned properties. The results for energy content decreased significantly (P>0.05) in across all the samples stored. The free fatty acid, acid valve and peroxide value increased significantly (P<0.05) in all the storage materials during the storage period but only the samples stored in PC and HDPE were within the recommended limit of FAO (Food and Agricultural Organization) at the end of the storage period. The sensory quality of the control sample was acceptable up to 12 weeks while samples in other storage materials were still acceptable at the end of the storage period under ambient storage condition.
Perception datasets for agriculture are limited both in quantity and diversity which hinders effective training of supervised learning approaches. Self-supervised learning techniques alleviate this problem, however, existing methods are not optimized for dense prediction tasks in agriculture domains which results in degraded performance. In this work, we address this limitation with our proposed Injected Noise Discriminator (INoD) which exploits principles of feature replacement and dataset discrimination for self-supervised representation learning. INoD interleaves feature maps from two disjoint datasets during their convolutional encoding and predicts the dataset affiliation of the resultant feature map as a pretext task. Our approach enables the network to learn unequivocal representations of objects seen in one dataset while observing them in conjunction with similar features from the disjoint dataset. This allows the network to reason about higher-level semantics of the entailed objects, thus improving its performance on various downstream tasks. Additionally, we introduce the novel Fraunhofer Potato 2022 dataset consisting of over 16,800 images for object detection in potato fields. Extensive evaluations of our proposed INoD pretraining strategy for the tasks of object detection, semantic segmentation, and instance segmentation on the Sugar Beets 2016 and our potato dataset demonstrate that it achieves state-of-the-art performance.