Open Journal Systems

Publication Frequency

Bi-annual

Journal Content

Search scope

Volume Arrangement

2024

2023

2022

Featured Articles

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Research status of agricultural robot technology

Peng Zhang, Lina Zhang, Duo Liu, Hongxin Wu, Bo Jiao

Article ID: 2058
Vol 2, Issue 1, 2021

DOI: https://doi.org/10.54517/ama.v2i1.2058
VIEWS - 82 (Abstract)

Abstract

According to the different agricultural production uses, agricultural robots were classified, mainly including agricultural information collection robots, pruning robots, grafting robots, transplanting robots, spraying robots and picking robots. The research status of mainstream agricultural robots at home and abroad were introduced, and their working principles and characteristics were expounded. Finally, the problems existing in the key technologies of existing agricultural robots and their future development directions were put forward.

Keywords

agricultural robot; research status; key technology; intelligent

Full Text:

PDF

References

1. Zhao Y, Wu C, Hu X, Yu G. Research progress and problems of agricultural robot. Transactions of the Chinese Society of Agricultural Engineering 2003; 19(1): 20–24.

2. Chen Z, Jiang S, Sun Y. Development status of agroforestry harvesting robots (Chinese). Agriculture Engineering 2019; 9(2): 10–18.

3. Zhao C. Prospects of agricultural robots (Chinese). China Rural Science and Technology 2019; 5: 20–21.

4. Nagasaka Y, Zhang Q, Grift TE, et al. Control system design for an autonomous field watching-dog robot. In: Proceedings of the International Conference on Automation Technology for Off-road Equipment (ATOE 2004); 7–8 October 2004; Kyoto, Japan.

5. Lida M, Kang D, Taniwaki M, et al. Localization of CO2 source by a hexapod robot equipped with an anemoscope and a gas sensor. Computers and Electronics in Agriculture 2008; 63(1): 73–80. doi: 10.1016/j.compag.2008.01.016

6. Bak T, Jakobsen H. Agricultural robotic platform with four wheel steering for weed detection. Biosystems Engineering 2004; 87(2): 125–136. doi: 10.1016/j.biosystemseng.2003.10.009

7. Deere Corporation. Robotic Vehicles and Methods for Soil Testing. U.S. Patent 02157038, 2 July 2003.

8. Winegood. The United States developed the second-generation grape pruning robot. Available online: http://weipai. vinehoo. com/blog/detail_51_15345 (accessed on 17 March 2012).

9. Jia T. Research on Positioning Method of Robotic Pruning Points for Grapevine Winter Pruning [Master’s thesis]. Zhejiang University of Technology; 2012.

10. Tong J, Ding Y, Wu C, et al. Design and experiment of key mechanism of single artificial seedling semi-automatic vegetable grafting machine. Transactions of the Chinese Society of Agricultural Machinery 2018; 49(10): 65–72.

11. Peng Y, Gu S, Chu Q, et al. Design of stock feeding device of grafting robot for solanaceae. Transactions of the Chinese Society of Agricultural Engineering 2016; 32(11): 76–82. doi: 10.11975/j.issn.1002-6819.2016.11.011

12. Peng Y. Design and Experiment of Key Mechanism of Solanaceous High-Speed Grafting Machine (Chinese) [Master’s thesis]. South China Agricultural University; 2016.

13. Kumar GVP, Raheman H. Development of a walk-behind type hand tractor powered vegetable transplanter for paper pot seedlings. Biosystems Engineering 2011; 110(2): 189–197. doi: 10.1016/j.biosystemseng.2011.08.001

14. Kang DH, Kim DE, Lee GI, et al. Development of a vegetable transplanting robot. Journal of Biosystems Engineering 2012; 37(3): 201–208. doi: 10.5307/JBE.2012.37.3.201

15. Zamani DM. Development and evaluation of a vegetable transplanter. In: Proceedings of the International Conference on Advances in Pure & Applied Sciences (ICAPAS 2014); 3–4 November 2014; Kuala Lumpur, Malaysia.

16. Compact and efficient case “Patriot” 3230 self-propelled sprayer (Chinese). Agricultural Machinery 2010; 1: 71.

17. Cai Z, Xiong H. John Deere 4630 self-propelled spraying machine (Chinese). Modern Agriculture 2014; 9: 48.

18. Xiong H. Introduction of Deer’s new R4038 self-propelled spraying machine (Chinese). Modern Agriculture 2014; 1: 49.

19. Song J, Zhang T, Xu L, Tang X. Research actuality and prospect of picking robot for fruits and vegetables. Transactions of the Chinese Society of Agricultural Machinery 2006; 37(5): 158–162.

20. Van Henten EJ, Van Tuijl BAJ, Hemming J, et al. Field test of an autonomous cucumber picking robot. Biosystems Engineering 2003; 86(3): 305–313. doi: 10.1016/j.biosystemseng.2003.08.002

21. Murakami N, Inoue K, Otsuka K. Selective harvesting robot of cabbage. JSAM 1995; 2: 24–31.

22. Wang W, Li C, Liu J, et al. Simulation of the walking mechanism of ground-air dual-use agricultural information gathering robot (Chinese). Journal of Agricultural Mechanization Research 2019; 41(6): 26–31.

23. Liu C, Zhang Y, Wang R, et al. Design of a small four-legged flexible robot for agricultural information collection (Chinese). Mechanical Design 2017; 34(12): 15–19.

24. Meng F, Liu T. Design of raspberry pruning robot (Chinese). Agricultural Mechanization Research 2019; 41(1): 138–142, 147.

25. Zhou T, Xu H, Liu J, Wang C. Design of two-wing rotating knife type grape summer shoot pruner (Chinese). Agriculture Development and Equipment 2019; 3: 101–102. doi: CNKI:SUN:NJJY.0.2019-03-079

26. Zhang K, Chu J, Zhang T, et al. Development status analysis of automatic grafting technology for vegetables. Transactions of the Chinese Society of Agricultural Machinery 2017; 48(3): 1–13. doi: 10.6041/j.issn.1000-1298.2017.03.001

27. Gu S. Development of 2JC-350 automatic grafting machine with cut grafting method for vegetable seedling. Transactions of the Chinese Society of Agricultural Engineering 2006; 12: 103–106.

28. Ma Z, Mou Y, Gu S, et al. Automatic grafting method of melon vegetables obliquely inserted (Chinese). Journal of Zhongkai Agricultural Engineering Institute 2012; 25 (1): 48–51.

29. Jiang K, Zheng W, Zhang Q, et al. Development and experiment of vegetable grafting robot (Chinese). Transactions of the Chinese Society of Agricultural Engineering 2012; 28(4): 8–14.

30. Chu J, Zhang L, Zhang T, et al. Design and experiment of grafting robot operated by one person for cucurbitaceous seedlings cultivated in plug trays. Transactions of the Chinese Society of Agricultural Machinery 2017; 48(1): 7–13. doi: 10.6041/j.issn.1000-1298.2017.01.002

31. Zhou T. Design and Experimental Research on Plug Seedling Transplanting Machine in Greenhouse (Chinese) [Master’s thesis]. Nanjing Agricultural University; 2009.

32. Han L, Mao H, Hu J, et al. Design and test of automatic transplanter for greenhouse plug seedings. Transactions of the Chinese Society of Agricultural Machinery 2016; 47(11): 59–67.

33. Cui Z, Chen Y, Guan C, et al. Experimental study on transplanting machine for substrate block seedlings (Chinese). Journal of Agricultural Mechanization Research 2019; 41(10): 158–161, 168.

34. Zhao Z, Mao H, Han L, Hu J. Design and test of lightweight semi-automatic transplanter. Journal of Agricultural Mechanization Research 2019; 41(10): 174–179.

35. 3WZG-3000A high ground clearance self-propelled spray bar sprayer (Chinese). Xinjiang Agricultural Mechanization 2010; 3: 40.

36. Guo T, Yang R, Li J, et al. Development of machine vision spray robot (Chinese). Chinese Journal of Agricultural Mechanization 2015; 36(5): 215–219.

37. Cao W, Zhang J, Li D, et al. Design of remote-controlled intelligent orchard spraying robot (Chinese). Hubei Agricultural Sciences 2018; 57(8): 122, 114–116.

38. Gu P, Shi W, Hu J, Jiang L. Design of intelligent spraying robot for vegetable greenhouses (Chinese). Electronic Technology 2018; 8: 72, 73–76. doi: CNKI:SUN:DZJS.0.2018-08-020

39. Lu H. Design and experiment of forest cone collection robot (Chinese). Transactions of the Chinese Society of Agricultural Machinery 2001; 32(6): 52–58. doi: 10.3969/j.issn.1000-1298.2001.06.015

40. Ji C, Feng Q, Yuan T, et al. Development and performance analysis of greenhouse cucumber picking robot system (Chinese). Robotics 2011; 33(6): 726–730.

41. Zhou T, Zhang D, Zhou M, et al. Design and implementation of tomato picking robot (Chinese). Anhui Agricultural Sciences 2018; 46(28): 182–185.

42. Gao Y, Liu J, Zhou Y. Design and experiment of small lifting picking robot (Chinese). Agricultural Mechanization Research 2019; 41(11): 132–137. doi: CNKI:SUN:NJYJ.0.2019-11-024

43. Zhou J, Zhou W, Liu J, et al. Review of research on automatic navigation robot in orchard. Computer and Digital Engineering 2019; 47(3): 571–576, 586.

44. Jiang G, He X, Du S, et al. Research progress of machine vision in autonomous navigation system of agricultural robots (Chinese). Journal of Agricultural Mechanization Research 2008; 30(3): 9–11.

45. Wang F. Design of autonomous obstacle avoidance for picking robot based on football game movement (Chinese). Journal of Agricultural Mechanization Research 2019; 41(8): 200–205.doi: 10.3969/j.issn.1003-188X.2019.08.035

46. Yuan Z, Shen Y. Research on trajectory optimization of fruit picking robot’s autonomous path finding and obstacle avoidance—Based on heuristic intelligent algorithm (Chinese). Agricultural Machinery Research 2017; 39(7): 204–208. doi: 10.3969/j.issn.1003-188X.2017.07.042

47. Guan Y, Yang X, Jiang T. Research progress on multi-sensor information fusion technology for agricultural robots (Chinese). Anhui Agricultural Sciences 2010; 25: 14127–14128, 14136. doi: 10.3969/j.issn.0517-6611.2010.25.211

48. Li X, Wang W, Lei T, Shen Y. Prospects of the application of multisensor information fusion techniques in agricultural engineering. Transactions of the Chinese Society of Agricultural Engineering 2003; 19(3): 10–13.

49. Li G, Ji C, Zhai L. Research progress and analysis of end-effector for fruits and vegetables picking robot. Chinese Agricul Tural Mechanization 2014; 35(5): 231–236, 240.

50. Peng Y, Liu YG, Yang Y, et al. Research progress on application of soft robotic gripper in fruit and vegetable picking. Transactions of the Chinese Society of Agricultural Engineering 2018; 34(9): 11–20. doi: 10.11975/j.issn.1002-6819.2018.09.002

Refbacks

There are currently no refbacks.

Editor-in-Chief

Dr. Joan Estrany

University of the Balearic Islands Spain

News & Announcements

2023-08-22

New version of Author Guidelines are update

Please follow the journal's author guideline and the required article template to prepare your manuscript

2022-09-10

The 2022 World Food Forum will be held in Italy

The 2022 World Food Forum (WFF) flagship event will take place from 17 to 21 October at the Headquarters of the Food and Agriculture Organization of the United Nations (FAO) in Rome, Italy and virtually.

2022-06-07

Italian researchers are protesting a law that may equate “biodynamic” methods with organic agriculture

One line in the draft law concerns scientists above all. Article I states that “for the purposes of this law, the biodynamic agriculture method […], when applied in compliance with the European Union’s dispositions on organic farming, is equated with organic farming”. A few articles later, the law requires one representative of biodynamic farmers to sit in a technical committee that will oversee the application of the law, including allocations of public funds.

More Announcements...

Section Collections

Proposal a Section Collection

Deadline: 2024-12-31

Food Science

Keywords: food science; functional foods; food ingredients; nutrient fortification; crop biomass; sustainable food; processing technologies; cross-disciplinary approach

Special Issue

Proposal a Special Issue

Deadline: 2023-08-24

Sustainability of modern agriculture

Keywords: sustainable agriculture, objectives of sustainable farming, involving devices, involving technology, pros and cons, etc.

Deadline: 2023-08-24

Technology in modern agriculture

Keywords: the agricultural device, the technology used in farming different plants, the impact of technology on agriculture, the evolution of agricultural technology, robotic systems, etc.