Hasil untuk "Agriculture (General)"

Shuai Xiang, Wei Guo, James Burridge et al.

Vision Foundation Models (VFM) pre-trained on large-scale unlabeled data have achieved remarkable success on general computer vision tasks, yet typically suffer from significant domain gaps when applied to agriculture. In this context, we introduce $SPROUT$ ($S$calable $P$lant $R$epresentation model via $O$pen-field $U$nsupervised $T$raining), a multi-crop, multi-task agricultural foundation model trained via diffusion denoising. SPROUT leverages a VAE-free Pixel-space Diffusion Transformer to learn rich, structure-aware representations through denoising and enabling efficient end-to-end training. We pre-train SPROUT on a curated dataset of 2.6 million high-quality agricultural images spanning diverse crops, growth stages, and environments. Extensive experiments demonstrate that SPROUT consistently outperforms state-of-the-art web-pretrained and agricultural foundation models across a wide range of downstream tasks, while requiring substantially lower pre-training cost. The code and model are available at https://github.com/UTokyo-FieldPhenomics-Lab/SPROUT.

Madhusmita Swain, Durgamadhab Mishra, Gourishankar Sahoo

Abstract Nanoparticles and Nanostructured materials are playing an ever-important role in affordable healthcare, environment remediation, renewable energy, agriculture, consumer electronics, cosmetics etc. However, progress in these sectors has to be sustainable, environmentally friendly and requires sustainable synthesis process of nanoparticles and nanomaterials with net zero toxic byproducts. Therefore, green synthesis techniques are being actively pursued by researchers everywhere. When naturally occurring precursors replace industrially produced chemicals; it is always cost effective and facilitates direct as well as indirect employments to common man. Zinc oxide (ZnO) is one of the few materials which has wide spread application in all of the above sectors due to its unique physical, chemical, optical and electronic properties. In this review, various green synthesis techniques for ZnO nanoparticles used by different researchers in last 5–8 years are discussed and reviewed. In the beginning, the conventional synthesis techniques of ZnO nanoparticles are discussed briefly including ball milling, sol–gel, hydrothermal and precipitation methods. In the second part, different green synthesis techniques are discussed using various plant extracts. Particularly, the use of green tea leaf extracts in ZnO nanoparticle synthesis is discussed in detail. The factors that affect the morphology of nanomaterials are also discussed. Finally, the challenges and issues still remaining to be addressed are outlined with a conclusion. The review will be useful to researchers who want to pursue green synthesis of nanoparticles in general and ZnO in particular as beginners. It will be beneficial to biochemist, biologist, biotechnologist, environmentalist, industrialist and policy makers interested in progress towards sustainable science and technology.

Taimoor Ahmad

The integration of unmanned aerial vehicles (UAVs) into smart agriculture has enabled real-time monitoring, data collection, and automated farming operations. However, the high mobility, decentralized nature, and low-power communication of UAVs pose significant security challenges, particularly in ensuring transaction integrity and trust. This paper presents a quantum-resilient blockchain framework designed to secure data and resource transactions in UAV-assisted smart agriculture networks. The proposed solution incorporates post-quantum cryptographic primitives-specifically lattice-based digital signatures and key encapsulation mechanisms to achieve tamper-proof, low-latency consensus without relying on traditional computationally intensive proof-of-work schemes. A lightweight consensus protocol tailored for UAV communication constraints is developed, and transaction validation is handled through a trust-ranked, multi-layer ledger maintained by edge nodes. Experimental results from simulations using NS-3 and custom blockchain testbeds show that the framework outperforms existing schemes in terms of transaction throughput, energy efficiency, and resistance to quantum attacks. The proposed system provides a scalable, secure, and sustainable solution for precision agriculture, enabling trusted automation and resilient data sharing in post-quantum eras.

Xufeng Cui, Ting Cai, W. Deng et al.

Agriculture is the foundation of the national economy, and achieving high-quality agricultural development is an important support for strong economic development in the post-pandemic era. Based on the new development philosophy of the Chinese government, this study constructs an evaluation framework of “innovation-coordination-green-openness-sharing” for high-quality agricultural development, and quantitatively assesses the level of high-quality agricultural development in China's Yangtze River Economic Belt with a systematic integration model, and explores the spatial evolution characteristics and obstacles of the level of high-quality agricultural development in Yangtze River Economic Belt. It reveals that the level of high-quality agricultural development in the Yangtze River Economic Belt shows a fluctuating upward trend in general, but there is variability among regions. The green dimension has the fastest development rate, followed by innovation and sharing. In terms of spatial characteristics, it gradually shows a pattern dominated by high levels and shows the characteristics of agglomeration, but the spatial correlation is not high. In terms of obstacle factors, openness and coordination are the main obstacle factors. Considering the different agricultural development models, it is suggested that international cooperation, new agricultural cooperation, and differentiated policies can be considered to promote high-quality agricultural development. This study provides a more complete evaluation framework for government policy-making authorities to measure the level of regional agricultural development and help regional agriculture achieve sustainable development at a higher quality level.

Qian Feng, Lu Lu, Qingyun Li et al.

Iron chlorin is known to affect plant growth, but its potential applications in watermelon production have rarely been explored. To better understand its effects on the growth, photosynthesis, yield, and quality of watermelon in a greenhouse setting, a series of experiments were conducted using the variety 'Sumeng 6'. At the flowering and early fruit expansion stages, the plants were sprayed with iron chlorin with mass concentrations of 0.001, 0.002, 0.004, and 0.008 μg/L (T1, T2, T3, and T4). Control plants were sprayed with water (CK). The growth index, root activity, photosynthetic pigment content, photosynthetic parameters, yield, and fruit quality of all plants were measured. The results showed: compared with CK, the T3-treated plants showed significant improvements in several aspects: the leaf contents of photosynthetic pigments, chlorophyll a, total chlorophyll, and carotenoid, increased by 19.51%, 14.29%, and 29.17%, respectively (P < 0.05); the net photosynthetic rate (Pn) increased by 23.60% (P < 0.05); and the soluble solids content, vitamin C content, and yield increased by 7.89%, 34.13%, and 16.27%, respectively (P < 0.05). In summary, it was found that spraying 0.004 μg/L iron chlorin on facility watermelon plants at the flowering and the early fruit expansion stages has a significant effect on the promotion of growth and development, leading to improved quality and yield. This study provides a theoretical reference and technical guide for high-quality and efficient watermelon production.

T. Turpurova, S. Kurbatov

The growth of the population in the world provides increased demand for soy and its processing products. Because in low-income countries, soy and its processing products are used as a cheap vegetable protein for human nutrition, and in developed countries - as a valuable protein raw material in animal feed. The article calculates the cost of 1 kg of raw protein in protein feeds of plant and animal origin. The cost of vegetable feed per 1 kg of protein is 1.5-3.5 times lower than the cost of animal feed. The dynamics of global soybean production are shown, which indicates that in 2023/24 it was about 395 million tons. The main soybeanproducing countries are Brazil, the USA, and Argentina, whose share in the total world production is more than 80%. The dynamics of soybean production and processing in Ukraine is presented. The largest soybean processors in Ukraine are such companies as Bunge, AdamPolSoya, Cargill, Kreativ OEP, Kyiv-Atlantic, Royal Taste, Vinnytsia OEP and others. The soybean market in Ukraine demonstrates significant success both in cultivation and in export and processing. Despite the difficult conditions caused by military actions and economic challenges, Ukrainian farmers were able to reach historical highs in the production and export of soybeans, ensuring high product quality and competitive prices on the world market. Soy is an export-oriented crop, about ? of which is delivered abroad in the form of raw materials or processed products. The largest importers of Ukrainian soybeans are the European Union, Egypt and Turkey. In connection with the innovations in the supply of soybeans to the EU, soybean producers must not only grow (use of high-quality, certified seed material, mineral fertilizers, general production and administrative costs), but also have a certificate of traceability and confirmation of the absence of deforestation throughout the production chain. Since the violation of EUDR provisions can lead to serious consequences and sanctions, namely fines of up to 4% of the total annual turnover of the enterprise for the previous financial year, confiscation of products, temporary exclusion from procurement procedures and access to public funding, including tender procedures and subsidies

Shanglong Yang, Zhipeng Yuan, Shunbao Li et al.

In the rapidly evolving field of artificial intelligence (AI), the application of large language models (LLMs) in agriculture, particularly in pest management, remains nascent. We aimed to prove the feasibility by evaluating the content of the pest management advice generated by LLMs, including the Generative Pre-trained Transformer (GPT) series from OpenAI and the FLAN series from Google. Considering the context-specific properties of agricultural advice, automatically measuring or quantifying the quality of text generated by LLMs becomes a significant challenge. We proposed an innovative approach, using GPT-4 as an evaluator, to score the generated content on Coherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and Exhaustiveness. Additionally, we integrated an expert system based on crop threshold data as a baseline to obtain scores for Factual Accuracy on whether pests found in crop fields should take management action. Each model's score was weighted by percentage to obtain a final score. The results showed that GPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories. Furthermore, the use of instruction-based prompting containing domain-specific knowledge proved the feasibility of LLMs as an effective tool in agriculture, with an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing pest management suggestions.

Heesup Yun, Sassoum Lo, Christine H. Diepenbrock et al.

Thermal cameras are an important tool for agricultural research because they allow for non-invasive measurement of plant temperature, which relates to important photochemical, hydraulic, and agronomic traits. Utilizing low-cost thermal cameras can lower the barrier to introducing thermal imaging in agricultural research and production. This paper presents an approach to improve the temperature accuracy and image quality of low-cost thermal imaging cameras for agricultural applications. Leveraging advancements in computer vision techniques, particularly deep learning networks, we propose a method, called $\textbf{VisTA-SR}$ ($\textbf{Vis}$ual \& $\textbf{T}$hermal $\textbf{A}$lignment and $\textbf{S}$uper-$\textbf{R}$esolution Enhancement) that combines RGB and thermal images to enhance the capabilities of low-resolution thermal cameras. The research includes calibration and validation of temperature measurements, acquisition of paired image datasets, and the development of a deep learning network tailored for agricultural thermal imaging. Our study addresses the challenges of image enhancement in the agricultural domain and explores the potential of low-cost thermal cameras to replace high-resolution industrial cameras. Experimental results demonstrate the effectiveness of our approach in enhancing temperature accuracy and image sharpness, paving the way for more accessible and efficient thermal imaging solutions in agriculture.

Kinyua Gikunda

This paper explores the transformative potential of artificial intelligence (AI) in the context of sustainable agricultural development across diverse regions in Africa. Delving into opportunities, challenges, and impact, the study navigates through the dynamic landscape of AI applications in agriculture. Opportunities such as precision farming, crop monitoring, and climate-resilient practices are examined, alongside challenges related to technological infrastructure, data accessibility, and skill gaps. The article analyzes the impact of AI on smallholder farmers, supply chains, and inclusive growth. Ethical considerations and policy implications are also discussed, offering insights into responsible AI integration. By providing a nuanced understanding, this paper contributes to the ongoing discourse on leveraging AI for fostering sustainability in African agriculture.

Thomas Cherico Wanger, Estelle Raveloaritiana, Siyan Zeng et al.

China is the leading crop producer and has successfully implemented sustainable development programs related to agriculture. Sustainable agriculture has been promoted to achieve national food security targets such as food self-sufficiency through the well-facilitated farmland construction (WFFC) approach. The WFFC is introduced in Chinas current national 10-year plan to consolidate farmlands into large and simplified production areas to maximise automation, and improve soil fertility and productivity. However, research suggests that diversified and smaller farms faciliate ecosystem services, can improve yield resilience, defuse human health threats, and increase farm profitability. Currently, WFFC has not considered ecological farmland improvements and it may miss long-term environmental benefits including ecosystem service preservation conducive to yields. Moreover, the nutritional status in China has changed in recent decades with undernutrition being dramatically reduced, but the prevalence of overweight, obesity, and chronic diseases being increased. While a strategic choice and management of crop and livestock species can improve nutrition, the environmental and production benefits of agricultural diversification are currently not well interlinked with Chinas food and nutrition security discussions. Lastly, the role of agricultural technology for socioeconomic benefits and the link with diversified agricultural production may provide vast benefits for food security. Here, we focus on the opportunities and co-benefits of agricultural diversification and technology innovations to advance food and nutrition security in China through ecosystem service and yield benefits. Our applied five-point research agenda can provide evidence-based opportunities to support China in reaching its ambitious food security targets through agricultural diversification with global ramifications.

I. Kranner, F. Minibayeva, R. Beckett et al.

P. Thornton, P. Jones, G. Alagarswamy et al.

Mauro Martini, Andrea Eirale, Brenno Tuberga et al.

Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and infield validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras. In this context, the contribution of this work is twofold: a synthetic dataset to train deep semantic segmentation networks together with a collection of virtual scenarios for a fast evaluation of navigation algorithms. Moreover, an automatic parametric approach is developed to explore different field geometries and features. The simulation framework and the dataset have been evaluated by training a deep segmentation network on different crops and benchmarking the resulting navigation.

Jayatilleke S. Bandara, Yiyong Cai

A. Amine, H. Mohammadi, I. Bourais et al.

A. Ismail, S. Takeda, P. Nick

Geli Zhang, Xiangming Xiao, Ç. Biradar et al.

Sheu-Usman Oladipo Akanbi, Olanrewaju Solomon Olatunji, Olamide Sulaiman Oladipo et al.

Climate unpredictability and weather extremes are being projected as capable of presenting additional challenges for farmers currently engaged in the low-technology based food production systems in sub-Saharan countries like Nigeria. This study assessed rice farming households’ vulnerability to climate change in Kwara State, Nigeria. Primary data, collected from 150 respondents using simple random sampling procedure were analysed employing descriptive statistic was use to describe the coping strategies adopted and Human Development Index (HDI) was created to assess vulnerability of rice farmers to climate change. Statistical analyses indicated a vulnerability assessment index of 0.3001, pointing to a fact that the zone is prone to the adverse effects of climatic variability. For this reason, the study empirically underscores the need for farmers to adopt and adapt the planting of drought tolerant and/or early maturing varieties of rice. Importantly, the capacities of the local communities needs to be strengthened vis-à-vis the relationship between climate change and crop production. Capacity building at the farm level is crucial for improving crop, soil and water management, enhancing the demand for and use of better and more efficient production inputs. Tied to farm-level capacity building is the need to refocus public agricultural-based institutions towards exposing the rice farmers to effective mitigation strategies in the wake of climate change, provision of agricultural inputs, expansion of irrigation, efficient and effective extension service delivery, market development and other forms of necessary support.

Ielyzaveta Lvova, Kateryna Kozmuliak, Liudmyla Strutynska-Struk

As climate change is one of the greatest challenges of our time, the legal and economic issues of global environmental security deserve high praise. In the area of industrial competitiveness, where the negative effects of global climate change include floods and droughts, forest fires, and rising sea levels, climate change is highly problematic. Climate impacts affect public and private agricultural infrastructure (including the coastal zone), resulting in lost productivity and increased costs for agriculture. The article applies climate change on a global scale in the form of greenhouse gas (GHG) emissions to determine how the mixtures and emissions of any one entity affect other areas (e.g., individual, community, company or country emissions). Exploring the theoretical and practical premises of climate change as a complex phenomenon, the novelty of this article is that it examines the current framework of the environmental-legal concept, not just the political implications of the legal framework. The research aim of the article lies in two dimensions: the European Union's current climate change policy framework (the climate and energy package, a set of climate change strategies and related policies targeting EU candidate countries); recent environmental operations in Ukraine as an EU candidate country under extraordinary conditions. This article examines recent changes in climate legislation and climate policy in EU member and candidate countries, as well as other highly developed countries, such as the United Kingdom, the United States, and China. Focusing on the impact of the EU Climate and Energy Package (2020 and 2030), this article examines the main implications of EU climate legislation regulating the EU Emissions Trading Scheme and promoting the role of renewable energy in global energy consumption and energy efficiency in general. As a result of this study, this analysis offers multifaceted conclusions based on the interaction of a number of current administrative acts on climate change and environmental policy on a global scale.

Arifah, Darmawan Salman, Amir Yassi et al.

Bulukumba Regency is one of the major rice-producing areas in South Sulawesi, Indonesia and has experienced frequent climate disasters over the past decade. Several downstream villages within the Bettu River irrigation area have been affected by the drought, culminating in reduced lowland rice production and increasing the vulnerability of farmers’ livelihoods. This study aims to evaluate the vulnerability of the livelihood system among rice farmers in the Bettu River irrigation area by classifying the area into two zones based on the distance from the main irrigation canal, namely the upstream area and downstream area. The livelihood vulnerability index (LVI) framework and livelihood vulnerability index-Intergovernmental Panel on Climate Change (LVI-IPCC) approach were applied by selecting geographic and socio-demographic indicators that affected the farmer households, including 8 major components and 26 sup-components. The data for LVI-IPCC estimation were collected by randomly selecting 132 households from villages in the two areas. The empirical results showed that farmers in the downstream area were more vulnerable to climate change than farmers in the upstream area. The major components causing the livelihood vulnerability of the downstream farmers were livelihood strategy, food, water, land, health, as well as natural disasters and climate variability. In particular, the sub-components of agricultural livelihood diversification, consistent water supply for farming, and drought events were important in the downstream area. Farmers in the upstream area were vulnerable to socio-demographic profile and social network components. The LVI-IPCC findings suggested that the government should prioritize farmers in the downstream area to develop resilience strategies, particularly by increasing irrigation infrastructure and the number of reservoirs and drilling holes. Furthermore, to increase their adaptive capacity in terms of diversification of agricultural livelihood systems, the government and donor agencies need to provide trainings on the development of home food industries for poor farmers and vulnerable households that were affected by disasters.