Compared with 1927, production per capita increased in 1928. Period preceding 1928.-Most of the major lines of production made substantial gains from the postwar depression within two or three years, and continued to grow at a relatively even pace thereafter, with mild recessions in 1924 and 1927. Per capita production in a few major lines, however, has been declining in recent years. Agriculture and animal husbandry.-Both the estimated crop yields and the volume of agricultural marketings increased in 1928. Mining.-Despite a continued high output of crude petroleum, the volume of mining as a whole declined slightly in 1928. Manufacturing.-Eight of the twelve major manufacturing groups, including those associated with building and automobilies, registered gains in 1928, and manufacturing as a whole regained the 1926 high level. Construction.-The volume of building per capita reached a new high level.
Worasit Sangjan, Piyush Pandey, Norman B. Best
et al.
Accurate identification of individual plants from unmanned aerial vehicle (UAV) images is essential for advancing high-throughput phenotyping and supporting data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction. MatchPlant enables end-to-end workflows by integrating UAV image processing, user-guided annotation, Convolutional Neural Network model training for object detection, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. In an early-season maize case study, MatchPlant achieved reliable detection performance (validation AP: 89.6%, test AP: 85.9%) and effectively projected bounding boxes, covering 89.8% of manually annotated boxes with 87.5% of projections achieving an Intersection over Union (IoU) greater than 0.5. Trait values extracted from predicted bounding instances showed high agreement with manual annotations (r = 0.87-0.97, IoU >= 0.4). Detection outputs were reused across time points to extract plant height and Normalized Difference Vegetation Index with minimal additional annotation, facilitating efficient temporal phenotyping. By combining modular design, reproducibility, and geospatial precision, MatchPlant offers a scalable framework for UAV-based plant-level analysis with broad applicability in agricultural and environmental monitoring.
As a social being, we have an intimate bond with the environment. A plethora of things in human life, such as lifestyle, health, and food are dependent on the environment and agriculture. It comes under our responsibility to support the environment as well as agriculture. However, traditional farming practices often result in inefficient resource use and environmental challenges. To address these issues, precision agriculture has emerged as a promising approach that leverages advanced technologies to optimise agricultural processes. In this work, a hybrid approach is proposed that combines the three different potential fields of model AI: object detection, large language model (LLM), and Retrieval-Augmented Generation (RAG). In this novel framework, we have tried to combine the vision and language models to work together to identify potential diseases in the tree leaf. This study introduces a novel AI-based precision agriculture system that uses Retrieval Augmented Generation (RAG) to provide context-aware diagnoses and natural language processing (NLP) and YOLOv8 for crop disease detection. The system aims to tackle major issues with large language models (LLMs), especially hallucinations and allows for adaptive treatment plans and real-time disease detection. The system provides an easy-to-use interface to the farmers, which they can use to detect the different diseases related to coffee leaves by just submitting the image of the affected leaf the model will detect the diseases as well as suggest potential remediation methodologies which aim to lower the use of pesticides, preserving livelihoods, and encouraging environmentally friendly methods. With an emphasis on scalability, dependability, and user-friendliness, the project intends to improve RAG-integrated object detection systems for wider agricultural applications in the future.
Agorakis Bompotas, Konstantinos Koutras, Nikitas Rigas Kalogeropoulos
et al.
The global agricultural sector is undergoing a transformative shift, driven by increasing food demands, climate variability and the need for sustainable practices. SUSTAINABLE is a smart farming platform designed to integrate IoT, AI, satellite imaging, and role-based task orchestration to enable efficient, traceable, and sustainable agriculture with a pilot usecase in viticulture. This paper explores current smart agriculture solutions, presents a comparative evaluation, and introduces SUSTAINABLE's key features, including satellite index integration, real-time environmental data, and role-aware task management tailored to Mediterranean vineyards.
Tannic acid (TA), a polyphenolic compound derived from plants, exhibits anti-inflammatory, antibacterial, antiviral, and antioxidant biological activities. Salmonella, a prevalent foodborne pathogen, poses a significant threat to poultry, resulting in considerable economic losses for the animal husbandry industry. In this study, we investigated the protective effects of TA against lung and intestinal injuries induced by a transient Salmonella infection in broilers. After a ten-day infection period, although Salmonella was not detected in the intestinal content of broilers, the infected broilers exhibited reduced body weight and compromised intestinal barrier function. Salmonella infection facilitated the growth of detrimental bacteria in the lungs and ileums, triggering an inflammatory response. TA inhibited the pathogen's colonization in the lungs and reduced serum levels of lipopolysaccharide (LPS) as well as lung levels of myeloperoxidase (MPO). Moreover, TA down-regulated the expression of pro-inflammatory cytokines and hindered the polarization of M1 macrophages in the lungs.In summary, TA mitigates Salmonella-induced lung inflammation and immune imbalance by its anti-inflammatory, antioxidant and antibacterial properties in broilers.
Mudathir Muhammad Salahudeen, Muhammad Auwal Mukhtar, Saadu Salihu Abubakar
et al.
Over time, agriculture is the most consistent activity, and it evolves every day. It contributes to a vast majority of the Gross Domestic Product (GDP) of Nigeria but as ironic as it may be, there is still hunger in significant parts of the country due to low productivity in the agricultural sector and comparison to the geometric population growth. During the first half of 2022, agriculture contributed about 23% of the country's GDP while the industry and services sector had a share of the remaining 77%. This showed that with the high rate of agricultural activities, Nigeria has not achieved food security for the teeming population. and more productivity levels can be attained. Technology can/will assist Nigeria in overcoming global poverty and hunger quicker in both rural and urban areas. Today, there are many types of agricultural technologies available for farmers all over the world to increase productivity. Major technological advancements include indoor vertical farming, automation, robotics, livestock technology, modern greenhouse practices, precision agriculture, artificial intelligence, and blockchain. Mobile phones have one of the highest adoption rates of technologies developed within the last century. Digitalization will bring consumers and farmers closer together to access the shortest supply chain possible and reduce rural poverty and hunger. The paper will review the different agricultural technologies and propose a mobile solution, code Sell Harvest, to make farming more sustainable and secure food. Keywords: Sell Harvest, Agriculture, Technology, Artificial Intelligence, and Digital Farming.
The integration of augmented reality (AR), extended reality (XR), and virtual reality (VR) technologies in agriculture has shown significant promise in enhancing various agricultural practices. Mobile robots have also been adopted as assessment tools in precision agriculture, improving economic efficiency and productivity, and minimizing undesired effects such as weeds and pests. Despite considerable work on both fronts, the combination of a versatile User Interface (UI) provided by an AR headset with the integration and direct interaction and control of a mobile field robot has not yet been fully explored or standardized. This work aims to address this gap by providing real-time data input and control output of a mobile robot for precision agriculture through a virtual environment enabled by an AR headset interface. The system leverages open-source computational tools and off-the-shelf hardware for effective integration. Distinctive case studies are presented where growers or technicians can interact with a legged robot via an AR headset and a UI. Users can teleoperate the robot to gather information in an area of interest, request real-time graphed status of an area, or have the robot autonomously navigate to selected areas for measurement updates. The proposed system utilizes a custom local navigation method with a fixed holographic coordinate system in combination with QR codes. This step toward fusing AR and robotics in agriculture aims to provide practical solutions for real-time data management and control enabled by human-robot interaction. The implementation can be extended to various robot applications in agriculture and beyond, promoting a unified framework for on-demand and autonomous robot operation in the field.
This review article explores the challenges and opportunities faced by the Bank for Agriculture and Agricultural Cooperatives (BAAC) in Thailand from a microfinance perspective. It examines the role of BAAC as a specialized financial institution in assisting underprivileged households and small businesses in accessing financial services. The study emphasizes the challenges and opportunities faced by BAAC in promoting sustainable development. It also explores BAAC's role in advancing the BCG Model policy, which fosters sustainability in the agricultural sector through Bio Economy Credit, Circular Economy Credit, and Green Credit. These initiatives support investments in biotechnology, waste reduction (Zero Waste), organic farming, and safe food production, all aimed at enhancing farmers' quality of life, stimulating growth in agriculture, and preserving the environment. Moreover, BAAC remains committed to upholding transparency, fairness, and operational standards.
Crop field detection is a critical component of precision agriculture, essential for optimizing resource allocation and enhancing agricultural productivity. This study introduces KonvLiNA, a novel framework that integrates Convolutional Kolmogorov-Arnold Networks (cKAN) with Nyström attention mechanisms for effective crop field detection. Leveraging KAN adaptive activation functions and the efficiency of Nyström attention in handling largescale data, KonvLiNA significantly enhances feature extraction, enabling the model to capture intricate patterns in complex agricultural environments. Experimental results on rice crop dataset demonstrate KonvLiNA superiority over state-of-the-art methods, achieving a 0.415 AP and 0.459 AR with the Swin-L backbone, outperforming traditional YOLOv8 by significant margins. Additionally, evaluation on the COCO dataset showcases competitive performance across small, medium, and large objects, highlighting KonvLiNA efficacy in diverse agricultural settings. This work highlights the potential of hybrid KAN and attention mechanisms for advancing precision agriculture through improved crop field detection and management.
Krish Didwania, Pratinav Seth, Aditya Kasliwal
et al.
Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types.
Clement E. Bohr, Marti Mestieri, Frederic Robert-Nicoud
As countries develop, the relative importance of agriculture declines and economic activity becomes spatially concentrated. We develop a model integrating structural change and regional disparities to jointly capture these phenomena. A key modeling innovation ensuring analytical tractability is the introduction of non-homothetic Cobb-Douglas preferences, which are characterized by constant unitary elasticity of substitution and non-constant income elasticity. As labor productivity increases over time, economic well-being rises, leading to a declining expenditure share on agricultural goods. Labor reallocates away from agriculture, and industry concentrates spatially, further increasing aggregate productivity: structural change and regional disparities are two mutually reinforcing outcomes and propagators of the growth process.
The integration of Internet of Things (IoT) technologies in agriculture holds promise for transforming farming practices, particularly in the Kingdom of Saudi Arabia (KSA). This study explores the adoption of smart farming practices among KSA farmers. Due to the geographical location and nature of KSA, it faces significant challenges in agriculture. The objective of this research is to discuss how IoT will enhance agriculture in KSA and identify its current usage by conducting a study on Saudi farmers with varying ages, regions, and years of experience. The results indicate that 90% of the farmers encounter challenges in farming, and all of them express interest in adopting smart farming to address these issues. While 60% of farmers are currently utilizing IoT technologies, they encounter challenges in implementing smart farming practices. Thus, smart farming presents solutions to prevalent challenges including adverse weather, water scarcity, and labor shortages, though barriers include cost and educational challenges.
Precision agriculture, also known as site-specific crop management, plays a crucial role in modern agriculture. Yield maps are an essential tool as they help identify the within-field variability that forms the basis of precision agriculture. If farmers could obtain yield maps for their specific site based on their field's soil and weather conditions, then site-specific crop management techniques would be more efficient and profitable. However, forecasting yield and producing reliable yield maps for an individual field can be challenging due to limited historical data. This paper proposes a novel two-stage approach for site-specific yield forecasting based on short-time series and high-resolution yield data. The proposed approach was successfully applied to predict yield maps at three different sites in Nebraska, demonstrating the method's ability to provide fine resolution and accurate yield maps.
A field experiment was undertaken at ICAR-AICRP on Spices, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal for four consecutive years i.e., from 2016-17 to 2019-20 to study the performance of twelve turmeric genotypes namely LTS01, LTS02, RH80, RH9/90, IT10, IT23, IT36, NDH11, NDH128, TCP191, TCP2 (Local check) and Prathiba (National Check) for growth, yield, dry recovery and foliar disease incidence. The experiment was laid out in randomized block design with three replications. The pooled data of the experiment revealed that the highest yield (38.73 t/ha) was recorded by TCP191 followed by IT10 (24.65 t/ha) and the lowest yield was recorded by IT23 (16.96 t/ha). Among the different evaluated genotypes, the highest dry recovery (22.63%) was recorded in TCP191 followed by TCP2 (22.00%) and the lowest was recorded in RH 9/90 (20.23%). With respect to leaf spot and leaf blotch, the lowest disease incidence was recorded by TCP191 (3.15 PDI & 2.61 PDI, respectively) followed by LTS1 (8.36 PDI, & 9.46 PDI, respectively). Thus, considering the yield and reaction to disease incidence of turmeric genotypes, TCP191 may be recommended for cultivation in the Terai zone of West Bengal, India.
Muhamad Rausyan Fikri, Taufiq Candra, Kushendarsyah Saptaji
et al.
The rapid development of technology has brought unmanned aerial vehicles (UAVs) to become widely known in the current era. The market of UAVs is also predicted to continue growing with related technologies in the future. UAVs have been used in various sectors, including livestock, forestry, and agriculture. In agricultural applications, UAVs are highly capable of increasing the productivity of the farm and reducing farmers' workload. This paper discusses the application of UAVs in agriculture, particularly in spraying and crop monitoring. This study examines the urgency of UAV implementation in the agriculture sector. A short history of UAVs is provided in this paper to portray the development of UAVs from time to time. The classification of UAVs is also discussed to differentiate various types of UAVs. The application of UAVs in spraying and crop monitoring is based on the previous studies that have been done by many scientific groups and researchers who are working closely to propose solutions for agriculture-related issues. Furthermore, the limitations of UAV applications are also identified. The challenges in implementing agricultural UAVs in Indonesia are also presented.
In this paper, we present an efficient solution for weed classification in agriculture. We focus on optimizing model performance at inference while respecting the constraints of the agricultural domain. We propose a Quantized Deep Neural Network model that classifies a dataset of 9 weed classes using 8-bit integer (int8) quantization, a departure from standard 32-bit floating point (fp32) models. Recognizing the hardware resource limitations in agriculture, our model balances model size, inference time, and accuracy, aligning with practical requirements. We evaluate the approach on ResNet-50 and InceptionV3 architectures, comparing their performance against their int8 quantized versions. Transfer learning and fine-tuning are applied using the DeepWeeds dataset. The results show staggering model size and inference time reductions while maintaining accuracy in real-world production scenarios like Desktop, Mobile and Raspberry Pi. Our work sheds light on a promising direction for efficient AI in agriculture, holding potential for broader applications. Code: https://github.com/parikshit14/QNN-for-weed
This study is aimed at the special working conditions of seeding on sloping land, combining advanced precision seeding technology and the structure of rotary hole filling corn precision metering device seed rowers at home and abroad, and studying soil entry characteristics, the characteristics of soil particles and the seed transport pattern in the puncture process, in order to improve the seed dispersal qualified index and reduce the coefficient of variation in the process of seeding. The simulation test of the cavity-tying device was carried out using the MBD–DEM coupling method, and it can be seen that the rocker bending angle is 120° when the force is the largest; at this time the rocker and the soil force is the largest, indicating the best effect on soil particle separation and the fastest movement speed. The single-factor test determined that the operating speed of the seed rower ranged from 0.8 to 1.2 m/s, the spring preload force of the seed rower ranged from 5.5 to 25 N, and the operating slope angle of the seed rower ranged from 8° to 16°. The optimal structure and parameter characteristics of the rotary hole filling corn precision metering device were determined with a multi-factor test, and it was proven that the rotary hole filling corn precision metering device has better performance and a higher seed rowing quality, with the qualified index reaching 96.2%. This study can provide a reference for the research of corn precision seeders, enrich the form of corn precision seeders, and effectively improve the level of corn mechanized seeding.
<i>Porphyromonas</i> spp. are oral anaerobic Gram-negative bacteria that form black-pigmented colonies on blood agar and produce volatile sulfur compounds (VSCs), such as hydrogen sulfide (H<sub>2</sub>S), methyl mercaptan (CH<sub>3</sub>SH), and dimethyl sulfide ((CH<sub>3</sub>)<sub>2</sub>S), which cause halitosis and the destruction of periodontal tissues. <i>P. gulae</i> is considered the main pathogen involved in periodontal disease in dogs. However, the characteristics of the VSCs produced by <i>P. gulae</i> are unknown. In the present study, VSCs were measured in 26 isolates of <i>P. gulae</i> and some isolates of the other <i>Porphyromonas</i> spp. obtained from the oral cavities of dogs with periodontal disease using an in vitro assay with an Oral Chroma<sup>TM</sup> gas chromatograph. The results demonstrated that <i>P. gulae</i> was able to produce large amounts of H<sub>2</sub>S and CH<sub>3</sub>SH, and the dominant product was CH<sub>3</sub>SH (CH<sub>3</sub>SH/H<sub>2</sub>S was approximately 2.2). Other <i>Porphyromonas</i> spp. that were also obtained from the oral cavities of dogs with periodontal disease indicated similar levels of production of H<sub>2</sub>S and CH<sub>3</sub>SH to those of <i>P. gulae</i>. It is strongly suggested that the high levels of H<sub>2</sub>S and CH<sub>3</sub>SH produced by <i>P. gulae</i> and other <i>Porphyromonas</i> spp. contribute to halitosis and the destruction of periodontal tissues during the progression of periodontal disease in dogs.
Gonzalo A. Collado, Moisés A. Valladares, Cristian Suárez
et al.
The capability to produce pearls is widespread in the phylum Mollusca, including bivalves of the superfamily Unionoidea. Here, we identified and characterized natural pearls formed by <i>Diplodon chilensis</i>, a freshwater clam native to southern South America, using samples obtained from two lakes located in the Chilean Patagonia. Pearls were studied using light and scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. Naturally formed pearls were found in both male and female <i>D. chilensis</i> specimens. Pearls are produced in different shapes, including spherical, ellipsoidal, buttoned, and bumpy, ranging in size from 200 µm to 1.9 mm. The internal microstructure is composed of irregular polygonal tablets, about 0.40 to 0.55 μm in thickness. EDX analysis showed that pearls are composed of calcium carbonate. FTIR and Raman spectra recorded several peaks attributable to the aragonite in pearls of this species, as has been shown in other mollusks. In addition to these results, pearls of different colors are illustrated.