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"Agricultural management"
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Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture
Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation can improve productivity and quality by reducing errors and adding flexibility to the work process. Primarily, machine vision technology has been used to develop crop production systems by detecting diseases more efficiently. This review provides a comprehensive overview of machine vision applications for stress/disease detection on crops, leaves, fruits, and vegetables with an exploration of new technology trends as well as the future expectation in precision agriculture. In conclusion, research on the advanced machine vision system is expected to develop the overall agricultural management system and provide rich recommendations and insights into decision-making for farmers.
Journal Article
A Review on Evapotranspiration Estimation in Agricultural Water Management: Past, Present, and Future
Evapotranspiration (ET) is a major component of the water cycle and agricultural water balance. Estimation of water consumption over agricultural areas is important for agricultural water resources planning, management, and regulation. It leads to the establishment of a sustainable water balance, mitigates the impacts of water scarcity, as well as prevents the overusing and wasting of precious water resources. As evapotranspiration is a major consumptive use of irrigation water and rainwater on agricultural lands, improvements of water use efficiency and sustainable water management in agriculture must be based on the accurate estimation of ET. Applications of precision and digital agricultural technologies, the integration of advanced techniques including remote sensing and satellite technology, and usage of machine learning algorithms will be an advantage to enhance the accuracy of the ET estimation in agricultural water management. This paper reviews and summarizes the technical development of the available methodologies and explores the advanced techniques in the estimation of ET in agricultural water management and highlights the potential improvements to enhance the accuracy of the ET estimation to achieve precise agricultural water management.
Journal Article
Asian agribusiness management : case studies in growth, marketing, and upgrading strategies
\"This book of case studies is designed to provide useful information for instructional purposes and for those interested in the management of Asian agribusiness. This collected volume of case studies is organized around three major themes--growth, marketing, and upgrading strategies. Many of the cases herein were used in Advanced Agribusiness Workshops jointly organized by the Asian Productivity Organization and Cornell University held in Bangkok, Manila, and Bali. Through a case study-driven approach, this book offers an opportunity for students, policymakers, and business owners to consider the impact of key trends like value-addition, urbanization, the environment, regional integration, climate change, and technology on Asian agribusinesses\"-- Provided by publisher.
Book
AI-driven optimization of agricultural water management for enhanced sustainability
Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best
R
2
compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.
Journal Article
Integrated drought management
\"The first volume of this comprehensive global perspective on Integrated Drought Management is focused on understanding drought, causes, and the assessment of drought impacts. It explains different types of drought: agricultural, meteorological, hydrological, and socio-economic droughts, their indices and the impact of climate change on drought. The volume also examines spatio-temporal analysis of drought, variability and patterns, assessment, and drought evaluation. With numerous case studies from India, Mexico, Turkey, Brazil, US, and other countries, this volume serves as a valuable resource for all readers who want to advance their knowledge on drought and risk management\"-- Provided by publisher.
BOOK
Effect of agricultural management system (“cash crop”, “livestock” and “climate optimized”) on nitrous oxide and ammonia emissions
The study aimed to measure soil-atmosphere N
2
O fluxes and their controlling factors, as well as NH
3
emissions and yields for two soils (silt loam and clay loam) in three management systems over two years under subsequent wheat and maize cultivation. The management systems were characterized as follows: (1) cash crop (C) with mineral fertilizer and conventional tillage; (2) livestock (L) with biogas residue fertilization and its incorporation prior to sowing in maize and reduced tillage; and (3) climate optimized (O) with minimum tillage, 8-year crop rotation, with biogas residue fertilization, in maize without incorporation in clay loam soil or incorporation by strip-tillage prior to seeding in silt loam soil. Stable isotope ratios of N
2
O and mineral N were determined to identify N
2
O processes. Within the organically fertilized maize treatments, cumulative N
2
O fluxes were highest in the O-system treatments of both sites (4.0 to 9.4 kg N ha
− 1
a
− 1
), i.e. more than twice as high as in the L-system (1.5 to 3.1 kg N ha
− 1
a
− 1
). Below root-strip till fertilizer application did not enhance N
2
O fluxes. Fluxes with mineral fertilization of wheat (1.1 to 3.1 kg N ha
− 1
a
− 1
) were not different from those with organic fertilization. Isotopic values of emitted N
2
O revealed that bacterial denitrification dominated most of the peak flux events, while the N
2
O/(N
2
+ N
2
O) ratio of denitrification was mostly between 0.1 and 0.5. It can be concluded that, contrary to the intention to lower greenhouse gas fluxes by the O-system management, the highest N
2
O fluxes occurred in the O-system without biogas digestate incorporation in maize. With respect to NH
3
fluxes, we could confirm that the application of digestate application in growing crops without incorporation or late incorporation in fertilization before sowing induces high fluxes. The beneficial aspects of the O-system including more stable soil structure and resource conservation, are thus potentially counteracted by increased N
2
O and NH
3
emissions.
Journal Article
A study of the impact of digital financial inclusion on the resilience of the agricultural chain
Based on the panel data of 30 provinces (except Tibet) from 2011 to 2020, the article examines the impact of digital financial inclusion on the resilience of the agricultural industry chain as well as the mechanism of its action. The results show that: (1) Digital inclusive finance promotes the enhancement of the resilience of the agricultural industry chain, in which the degree of influence on “marketing resilience” and “distribution resilience” is much larger than that on “production resilience,” “processing resilience,” and “product resilience.” The degree of impact on “marketing resilience” and “distribution resilience” is much larger than “production resilience,” “processing resilience,” and “product resilience.” (2) Digital inclusive finance can promote the resilience of the agricultural industry chain through the promotion of agricultural technology innovation and the development of new agricultural business entities. (3) The enhancement of the resilience of the agricultural industry chain is more significant in the eastern region, where the level of digital inclusive finance is high. Based on the results of the study, the article puts forward relevant suggestions in terms of promoting the in-depth integration of digital inclusive finance and the agricultural industry chain, increasing the support for new agricultural business subjects, and strengthening agricultural technological innovation to promote sustainable development of the agricultural industry chain.
Journal Article