An evolutionary approach to robot scheduling in protected cultivation systems for uninterrupted and maximization of working time

Uyeh, Daniel Dooyum; Pamulapati, Trinadh; Mallipeddi, Rammohan; Park, Tusan; Woo, Seungmin; Lee, Siyoung; Lee, Jongwon; Ha, Yushin

doi:10.1016/j.compag.2021.106231

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…For instance, small agricultural robots can improve weeding efficiency and work optimally with little interruption. By doing so, robots can reduce soil compaction and erosion [15]. Big data analytic techniques can be employed to assist farmers in the decision-making process, such as the application of irrigation water, fertilizer, and herbicides [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Affecting the Adoption of Digital Technology by Farmers in China: A Systematic Literature Review

Cui,

Wang

2023

Sustainability

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Increasing pressure for food security and environmental sustainability has highlighted the need to switch from conventional agricultural methods to advanced agricultural practices. Digital agricultural technologies are considered promising solutions for sustainable intensification of food production and environmental protection. Despite significant promotional efforts initiated in recent years in China, the adoption rate remains low. The objective of this study is to gain insight into the factors affecting the adoption of on-farm digital technologies in China using a systematic review approach that analyzes 10 relevant studies. Data regarding methodological aspects and results are extracted. We identify 19 key adoption drivers that are related to socioeconomic, agroecological, technological, institutional, psychological, and behavioral factors. There is a predominance of ex-ante studies that use stated preference methods. We conclude with a discussion of the design of policy incentives to induce the adoption of digital technologies. Additionally, the review points to the limitations of existing research and suggests approaches that can be adopted for future investigations. This review provides meaningful implications for the development of future efforts to promote digital transformation for sustainable agriculture in China.

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Section: Literature Reviewmentioning

confidence: 99%

Factors Affecting the Adoption of Digital Technology by Farmers in China: A Systematic Literature Review

Cui,

Wang

2023

Sustainability

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“…This property is especially important because herbicide effectiveness at this crop stage can be limited because of the size of weeds, the absorption of herbicides, and potential for crop injury (Livingston et al., 2016). Robots can improve weeding efficiency and work long hours in an optimized manner with uninterrupted and predictable performance (Uyeh et al., 2021). In addition, because of their size and weight, these robots do not cause soil compaction and erosion.…”

Section: The Imperative For Digital Agriculture For Sustainable Agric...mentioning

confidence: 99%

Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges

Khanna

Atallah

Kar

et al. 2022

Agricultural Economics

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Agriculture faces key challenges of increasing productivity while reducing adverse impacts on the environment. Conventional practices that rely on tillage, inefficient and over-application of chemicals, and monoculture row cropping are leading to growing resistance of weeds and pests to chemicals, nutrient and sediment run-off, and declining soil carbon stocks in the United States. Digital technologies and artificial intelligence (AI) technologies are enabling the collection of vast amounts of geo-referenced information about growing conditions within the field, automated implementation of spatially varying input applications, and reduced reliance on chemical inputs. We discuss the pathways by which digital agricultural technologies have the potential to address the challenge of herbicide-resistant weeds, over-application of nitrogen and irrigation water, and cover crop planting for restoring soil health and contribute to the environmental sustainability of agriculture. Then, we discuss the economic factors, behavioral preferences of farmers, peer pressure, and social networks that can be expected to play a role in adoption decisions. We conclude with a discussion of approaches for ex ante assessments of the determinants of farmer willingness to adopt digital technologies and their diffusion in a region.

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“…This is done to improve monitoring and control of micro-climate parameters and sometimes facilitate remote-controlled and autonomous cultivation. Decisions may be made based on various actuators used to regulate heating, lighting, cooling, dosing of CO 2 and fertilizers, dehumidification, irrigation, screening, fogging, as examples ( Nelson, 1991 ; Uyeh et al, 2019 , 2021 ; Bhujel et al, 2020 ; Gadekallu et al, 2021 ). These actuators operate based on sensors providing feedback on measured data for the control loop set points configured in a computing device ( Stanghellini, 2013 ; Graamans et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…Multiclass models have been used to develop multivariate statistical methods in agriculture ( Gadekallu et al, 2021 ) and principal whale optimization-based neural networks to classify diseases in plants ( Gadekallu et al, 2021 ). Others include algorithms and systems for improved decision-making and optimizations ( Park et al, 2019 ; Syed and Hachem, 2019a , b ; Uyeh et al, 2019 , 2021 ; Bhujel et al, 2020 ). Using an inadequate number of sensors may lead to under-performance, while a likely result of being superfluous is large sizes of redundant data and its associated management problems.…”

Section: Introductionmentioning

confidence: 99%

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

et al. 2022

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Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled. Key to indoor environment monitoring and control and potentially reducing operational costs are the strategic placement of an optimal number of sensors using a robust method. A multi-objective approach based on supervised machine learning was used to determine the optimal number of sensors and installation positions in a protected cultivation system. Specifically, a gradient boosting algorithm, a form of a tree-based model, was fitted to measured (temperature and humidity) and derived conditions (dew point temperature, humidity ratio, enthalpy, and specific volume). Feature variables were forecasted in a time-series manner. Training and validation data were categorized without randomizing the observations to ensure the features remained time-dependent. Evaluations of the variations in the number and location of sensors by day, week, and month were done to observe the impact of environmental fluctuations on the optimal number and location of placement of sensors. Results showed that less than 32% of the 56 sensors considered in this study were needed to optimally monitor the protected cultivation system’s internal environment with the highest occurring in May. In May, an average change of −0.041% in consecutive RMSE values ranged from the 1st sensor location (0.027°C) to the 17th sensor location (0.013°C). The derived properties better described the ambient condition of the indoor air than the directly measured, leading to a better performing machine learning model. A machine learning model was developed and proposed to determine the optimal sensors number and positions in a protected cultivation system.

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An evolutionary approach to robot scheduling in protected cultivation systems for uninterrupted and maximization of working time

Cited by 6 publications

References 37 publications

Factors Affecting the Adoption of Digital Technology by Farmers in China: A Systematic Literature Review

Factors Affecting the Adoption of Digital Technology by Farmers in China: A Systematic Literature Review

Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

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