Sensor and vision based autonomous AGRIBOT for sowing seeds

Santhi, Palepu V.; Kapileswar, N.; Chenchela, Vijay K. R.; Prasad, C H Venkata Siva

doi:10.1109/icecds.2017.8389873

Cited by 41 publications

(7 citation statements)

References 15 publications

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“…The adoption of autonomous technologies has the potential to reduce operational time and costs, while also mitigating worker discomfort and strain [12]. Embedded system-controlled seed cum fertilizer applicator not only holds promise for increased farmer productivity and profitability but also fosters improved working conditions, ultimately contributing to the long-term sustainability and growth of the agricultural industry.…”

Section: Conventional Methods Vs Embedded System-controlled Seed Cum ...mentioning

confidence: 99%

Efficiency and Cost-effectiveness Analysis of Developed Embedded System-Controlled Seed and Fertilizer Applicator

Kumar,

Kushwaha,

Kumar

et al. 2024

JEAI

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In maize cultivation, the labour-intensive, time-consuming, and costly nature of manual planting necessitates the exploration of mechanized solutions. This study presents a comprehensive economic evaluation of an embedded system-controlled seed cum fertilizer applicator as a potential answer to these challenges. Precise planting methods offer a multi-pronged approach, aiming to reduce cultivation costs, minimize time requirements, and improve worker comfort during planting operations. By meticulously analyzing both ownership and operational expenses, this study empowers farmers with the knowledge necessary to make informed decisions. These decisions could involve adopting the precise seed cum fertilizer applicator technology, optimizing existing machinery, or exploring alternative methods to achieve enhanced farm productivity and financial gains. The embedded system-controlled seed cum fertilizer applicator was directly compared to traditional manual planting methods. This analysis revealed ownership and operating costs of 9.56 Rs/h and 63 Rs/h, respectively. The compelling findings demonstrated a remarkable 96.87% reduction in planting time and a 55.47% cost decrease when utilizing the embedded system compared to manual methods. These results highlight the significant cost-effectiveness and environmental benefits associated with adopting this precise planting system. This research offers valuable insights to farmers, potentially promoting the integration of advanced agricultural technologies that can optimize resource utilization, improve operational efficiency, and contribute to the implementation of sustainable farming practices.

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Section: Conventional Methods Vs Embedded System-controlled Seed Cum ...mentioning

confidence: 99%

Efficiency and Cost-effectiveness Analysis of Developed Embedded System-Controlled Seed and Fertilizer Applicator

Kumar,

Kushwaha,

Kumar

et al. 2024

JEAI

View full text Add to dashboard Cite

show abstract

“…Further, several noncontact sensing methods for determining the seed flow rate are proposed in [16], "where the sensors were equipped with LEDs, infrared, visible light, laser-LED, and a radiation reception element". The output voltage fluctuates depending on how the seeds move through the sensor and band of light rays and how the shades fall on the reception parts [17].…”

Section: Review Of Literaturementioning

confidence: 99%

Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling

Sharma

Sharma²,

Sharma³

et al. 2022

Journal of Sensors

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Agriculture has become more industrialized and intensive due to the rising demand for food in quality and quantity. Agricultural modernization will be made possible by the Internet of Things (IoT), a technology with a great promise for revolutionizing the industry. Agricultural products will be in high demand by 2050 due to a 30% increase in the global population, so there is a need to devise new mechanisms for agriculture, and smart agriculture is one of those mechanisms; however, smart agriculture needs to be explored further to realize its potential fully. So, to explore the potential of this field, the researchers have used a corpus that is extracted from the Scopus database from the year 2008 to the year 2022 and applied the LDA technique. A corpus of 4309 articles was selected from the Scopus database to apply the latent Dirichlet analysis (LDA) model to predict research areas for smart agriculture. Using IoT technology, farmers and producers may better manage their resources, such as fertilizer consumption and the number of trips made by farm vehicles, while minimizing waste and maximizing productivity, including water, electricity, and other inputs. This data-driven experimental study identifies smart agriculture research trends by implementing a topic modeling technique previously used in smart agriculture. The authors have created seventeen research themes in smart agriculture based on the LDA topic modeling. This analysis suggests that the indicated areas are in the growth phase and require further research and exploration.

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“…The term Precision Agriculture is described as being "a management strategy that uses electronic information and other technologies to gather, process, and analyze spatial and temporal data for the purpose of guiding targeted actions that improve efficiency, productivity, and sustainability of agricultural operations" [1] . Robotic technology is a powerful tool to realize targeted actions in agriculture, which usually rely on machine vision or other sensing technologies to perform selective or targeted working of seed sowing [77] , Seedling sorting/replanting [78] , spraying [79,80] weeding [81] , pruning [82,83] , trimming [60,[84][85][86] , flower or fruit thinning [87,88] , fruit bagging [87,89,90] , de-leafing [91,92] , pollination [93][94][95][96][97] , bin-handling [98][99][100] , fruit transportation [101,102] , fruit grading [103,104] , etc. (Table 5).…”

Section: Other Selective Working Robots In Crop-farmingmentioning

confidence: 99%

Development status and trend of agricultural robot technology

Jin¹,

Liu²,

Xu³

et al. 2021

International Journal of Agricultural and Biological Engineering

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In the face of the contradiction between the increasing demand for agricultural products and the sharp reduction of agricultural resources and labor force, agricultural robot technology is developing explosively on the basis of decades of technical and industrial exploration. In view of the complexity and particularity of the development of agricultural robot technology, it is of great value to summarize its development characteristics and make reasonable judgments on its development trend. In this paper, the type of agricultural robot systems was first discussed. From the classification of agricultural robot systems, the development of main types of monitoring robots, non-selective and selective working robots for crop farming, livestock and poultry farming and aquaculture were introduced in detail. Then the scientific research, core technology, and commercialization of different types of agricultural robots were summarized. It is believed that navigation in complex agricultural environments, damage-free robot-crop interaction, and agronomy-robot fusion have high scientific value and significance to promote the revolutionary advances in agricultural robot technology. The characteristics of inter-discipline between agricultural robot technology and new materials, artificial intelligence, bionics, agronomy are research focus. The fast damage-free operation, autonomous navigation for complex environments, target detection for complex backgrounds, and special design for agricultural robots are considered to be the key technology of agricultural robot development, and the development path is given. Finally, robot-crop interaction simulation, big data support, and artificial intelligence are regarded as paths to realize the breakthrough of key agricultural robot technologies. The summary and prospect of this paper are of positive significance to promote the overall development of agricultural robot technology.

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Sensor and vision based autonomous AGRIBOT for sowing seeds

Cited by 41 publications

References 15 publications

Efficiency and Cost-effectiveness Analysis of Developed Embedded System-Controlled Seed and Fertilizer Applicator

Efficiency and Cost-effectiveness Analysis of Developed Embedded System-Controlled Seed and Fertilizer Applicator

Broadening the Research Pathways in Smart Agriculture: Predictive Analysis Using Semiautomatic Information Modeling

Development status and trend of agricultural robot technology

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