Robotics and Automation in Agriculture: Present and Future Applications

Mohd Saiful Azimi Mahmud, Mohamad Shukri Zainal Abidin, Abioye Abiodun Emmanuel, Hameedah Sahib Hasan


Agriculture is the backbone of society as it mainly functions to provide food, feed and fiber on which all human depends to live. Precision agriculture is implemented with a goal to apply sufficient treatments at the right place in the right time with the purpose to provide low-input, high efficiency and sustainable agricultural production. In precision agriculture, automation and robotics have become one of the main frameworks which focusing on minimizing environmental impact and simultaneously maximizing agricultural produce. The application of automation and robotics in precision agriculture is essentially implemented for precise farm management by using modern technologies. In the past decades, a significant amount of research has focused on the applications of mobile robot for agricultural operations such as planting, inspection, spraying and harvesting. This paper reviews the recent applications of automation and robotics in agriculture in the past five years. In this paper, the recent implementations are divided into four categories which indicates different operations executed for planting management starting from a seed until the product is ready to be harvested. Towards the end of this paper, several challenges and suggestions are described to indicate the opportunities and improvements that can be made in designing an efficient autonomous and robotics system for agricultural applications. Based on the conducted review, different operations have different challenges thus require diverse solutions to solve the specific operational problem. Therefore, the development process of an efficient autonomous agricultural robotic system must consider all possibilities and challenges in different types of agricultural operation to minimize system errors during future implementation. In addition, the development cost needs to be fully considered to ensure that the farmers will be able to invest their capital as a consumer. Therefore, it will become highly possible for the autonomous agricultural robotic system to be widely implemented throughout the world in the future.


Agriculture; Applications; Automation; Food security; Robotics.

Article Metrics

Abstract view : 4631 times
PDF - 1622 times

Full Text:



Sustainable Development Indicator Group., 1996 (accessed 07.03.2020).

Science and Technology Options Assessment (Precision Agriculture)., 2020 (accessed 07.03.2020).

Automated Agriculture: Robots and the future of Farming., 2017 (accessed 07.03.2020).

A. Ruckelshausen, P. Biber, M. Dorna, H. Gremmes, R. Klose, A. Linz, et al., BoniRob: An autonomous field robot platform for individual plant phenotyping, Precision Agriculture, 9(841), 2009, 841-847.

M. Stein, S. Bargoti and J. Underwood, Image based mango fruit detection, localization and yield estimation using multiple view geometry, Sensors, 16(11), 2016, 1-25.

J. Roca, M. Comellas, J. Pijuan and M. Nogues, Development of an easily adaptable three-point hitch dynamometer for agricultural tractors. Analysis of the disruptive effects on the measurements, Soil and Tillage Research, 194, 2019, 1-11.

Agras MG-1S Series., 2020 (accessed 07.03.2020).

C. Reza Karmulla, K. Seyyed Hossein, R-K. Hossein, M. Alireza and M. Motjaba, Predicting header wheat loss in a combine harvester, a new approach, Journal of the Saudi Society of Agricultural Sciences, 19, 2020, 179-184.

S. N. Neha, V. S. Virendra and R. D. Shruti, Precision agriculture robot for seeding function, 2016 International Conference on Inventive Computation Technologies, Coimbatore, India, 2016, 1-8.

C. Anil, K. Habib and M. Sahin, Development of an electro-mechanic control system for seed-metering unit of single seed corn planters Part I: Design and laboratory simulation, Computers and Electronics in Agriculture, 144, 2018, 71-79.

C. Anil, K. Habib and M. Sahin, Development of an electro-mechanic control system for seed-metering unit of single seed corn planters Part II: Field performance, Computers and Electronics in Agriculture, 145, 2018, 11-17.

F. Weiqiang, G. Na’na, A. Xiaofei and Z. Junxiong, Study on precision application rate technology for maize no-tillage planter in North China plain, IFAC-PapersOnLine, 51(17), 2018, 412-417.

H. Xiantao, D. Youqiang, Z. Dongxing, Y. Li, C. Tao and Z. Xiangjun, Development of a variable-rate seeding control system for corn planters Part I: Design and laboratory experiment. Computers and Electronics in Agriculture, 162, 2019, 318-327.

H. Xiantao, D. Youqiang, Z. Dongxing, Y. Li, C. Tao and Z. Xiangjun, Development of a variable-rate seeding control system for corn planters Part II: Field performance, Computers and Electronics in Agriculture, 162, 2019, 309-317.

A. Zahra and M. Saman Abdanan, Real time laboratory and field monitoring of the effect of the operational parameters on seed falling speed and trajectory of pneumatic planter. Computers and Electronics in Agriculture, 145, 187-198, 2018.

Y. Shi, S. Xin, X. Wang, Z. Hu, N. David and W. Ding, Numerical simulation and field tests of minimum-tillage planter with straw smashing and strip laying based on EDEM software, Computers and Electronics in Agriculture, 166, 2019, 105021.

Z. M. Khazimov, G. C. Bora, K. M. Khazimov, M. Z. Khazimov, I. B. Ultanova and A. K. Niyazbayev, Development of a dual action planting and mulching machine for vegetable seedlings, Engineering in Agriculture, Environment and Food, 11(2), 2018, 74-78.

S. Khwantri, C. Choochart, K. Khanita, K. Pornnapa, C. Somchai and T. Eizo, Effect of metering device arrangement to discharge consistency of sugarcane billet planter, Engineering in Agriculture, Environment and Food, 11(3), 2018, 139-144.

F. Philip Brune, J. Bradley Ryan, T. Frank Technow and D. Brenton Myers, Relating planter downforce and soil strength, Soil and Tilage Research, 184, 2018, 243-252.

B. Bahram, N. Hossein, K. Hadi, B. Hossein and E. Iraj, Development of an infrared seed-sensing system to estimate flow rates based on physical properties of seeds, Computers and Electronics in Agriculture, 162, 2019, 874-881.

A. A. Ari, A. Gueroui, N. Labraoui and B. O. Yenke, Concepts and evolution of research in the field of wireless sensor networks, International Journal of Computer Networks & Communications, 7(1), 2015, 81-98.

T. Mukesh Kumar and D. Dhananjay Maktedar, A role of computer vision in fruits and vegetables among various horticulture products of agriculture fiels: A survey, Information Processing in Agriculture (In Press)

S. Mahajan, A. Das and H. K. Sardana, Image acquisition techniques for assessment of legume quality, Trends in Food Science & Technology, 42(2), 2015, 116-133.

S. Vijay and A. K. Misra, Detection of plant leaf disease using image segmentation and soft computing techniques, Information Processing in Agriculture, 4(1), 2017, 41-49.

O. Mehmet Metin and A. Kemal, Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms, Physica A: Statistical Mechanics and its Applications, 535, 2019, 122537.

S. Vijay, Sunflower leaf disease detection using image segmentation based on particle swarm optimization, Artificial Intelligence in Agriculture, 3, 2019, 62-68.

N. Chao, W. Dongyi, V. Robert, H. Maxwell and T. Yang, Automatic inspection machine for maize kernels based on deep convolutional neural networks, Biosystems Engineering, 178, 2019, 131-144.

R. Karthik, M. Hariharan, A. Sundar, M. Priyanka, J. Annie and R. Menaka, Attention embedded residual CNN for disease detection in tomato leaves, Applied Soft Computing, 86, 2020, 105933.

K. Wan-Soo, L. Dae-Hyun and K. Yong-Joo, Machine vision-based automatic disease symptom detection of onion downly mildew, Computers and Electronics in Agriculture, 168, 2020, 105099.

B. Damian, J. A. Matt, K. L. Alison, G. Christopher and N. Roy, Detection and differentiation between potato (Solanum tuberosum) diseases using calibration models trained with non-imaging spectrometry data, Computers and Electronics in Agriculture, 167, 2019, 105056.

K. Kerim, E. T. Mehmet, T. Ramazan and B. Aysin, Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance, Sustainable Computing: Informatics and Systems (In Press).

S. Muhammad, K. Muhammad Attique, I. Zahid, A. Muhammad Faisal, M. Ikram Ullah Lali and J. Muhammad Younus, Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection, Computers and Electronics in Agriculture, 150, 2018, 220-234.

T. R. Hafiz, A. S. Basharat, M. Ikram Ullah Lali, A. K. Muhammad, S. Muhammad and A. C. B. Syed, A citrus fruits and leaves dataset for detection and classification in citrus disease through machine learning, Data in Brief, 26, 2019, 104340.

C. Tingting, Z. Jialei, C. Yong, W. Shubo and Z. Lei, Detection of peanut leaf spots disease using canopy hyperspectral reflectance, Computers and Electronics in Agriculture, 156, 677-683, 2019.

T. Xiaoqian, L. Peiwu, Z. Zhaowei, Z. Qi, G. Jingnan and Z. Wen, An ultrasensitive gray-imaging-based quantitative immunochromatographic detection method for fumonisin B1 in agricultural products, Food Control, 80, 2017, 333-340.

E. Suganya, S. Sountharrajan, S. Shishir Kumar and M. Karthiga, IoT in agriculture investigation on plant diseases and nutrient level using image analysis techniques, Internet of Things in Biomedical Engineering, 2019, 117-130.

K. Ahmed, E. D. Serag Habib, I. Haythem, Z. Sahar, F. Yasmine and M. Mohamed Khairy, An IoT-based cognitive monitoring system for early plant disease forecast, Computers and Electronics in Agriculture, 166, 2019, 105028.

G. Ping, D. Puwadol Oak and Y. Shimon Nof, Agricultural cyber physical system collaboration for greenhouse stress management. Computers and Electronics in Agriculture, 150, 2018, 439-454.

S. Sanat, U. Jayalakshmi and K. Subrat, Automation of agriculture support systems using Wisekar: Case study of a crop-disease advisory service, Computers and Electronics in Agriculture, 122, 2016, 200-210.

O. Roberto, M. Massimo, P. Tirelli, C. Aldo, I. Marcello, T. Emanuele, H. Marko, B. Joerg, P. Julian, S. Christoph and U. Heinz, Selective spraying of grapevines for disease control using a modular agricultural robot, Biosystems Engineering, 146, 2016, 203-215.

M. E. R. Paice, P. C. H Miller and W. Day, Control requirements for spatially selective herbicide sprayers, Computers and Electronics in Agriculture, 14(2), 1996, 163-177.

D. C. Slaughter, D. K. Giles and C. Tauzer, Precision offset spray system for roadside weed control. Journal of Transportation Engineering, 125, 1999, 364-371.

O. Roberto, M. Massimo, T. Paolo, C. Aldo, I. Marcello, H. Marko, B. Joerg, P. Julian, S. Christoph and U. Heinz, The CROPS agricultural robot: Application to selective spraying of grapevine’s diseases, International Conference of Agricultural Engineering, Zurich, Switzerland, 2014, 1-8.

M. Hossein, M. Saeid, G. Barat and M. Hassan, Ultrasonic sensing of pistachio canopy for low-volume precision spraying, Computers and Electronics in Agriculture, 112, 2015, 149-160.

M. M. Elizabeth, Q. Lijun and W. Yalei, Spray deposition and distribution on the targets and losses to the ground as affected by application volume rate, airflow rate and target position, Crop Protection, 116, 2019, 170-180.

U. Trygve, U. Frode, B. Anders, D. Jarle, N. Jan, O. Oyvind, W. B. Therese and T. G. Jan, Robotic in-row weed control in vegetables, Computers and Electronics in Agriculture, 154, 2018, 36-45.

S. Danielle, M. Luisa and M. Vieri, Development of a prototype of telemetry system for monitoring the spraying operation in vineyards, Computers and Electronics in Agriculture, 142(A), 2017, 248-259.

G. Marco, M. Paolo, M. Marco, G. Montserrat and B. Paolo, Effect of sprayer settings on spray drift during pesticide application in poplar plantations (Populus spp.), Science of The Total Environment, 578, 2017, 427-439.

A. V. Keyvan and M. Jafar, A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops, Computers and Electronics in Agriculture, 139, 2017, 153-163.

M. Ales, D. Matevz, S. Brane, O. Roberto and H. Marko, Close-range air-assisted precision spot-spraying for robotic applications: Aerodynamics and spray coverage analysis, Biosystems Engineering, 146, 2016, 216-226.

G. S. Mariano, E. Luis, P. R. Manuel, A. Juan and G. S. Pablo, Autonomous systems for precise spraying-Evaluation of a robotized patch sprayer. Biosystems Engineering, 146, 2016, 165-182.

C. Jasmine and B. Stuart, Evaluation of a spray scheduling system. IFAC-PapersOnLine, 49(16), 2016, 226-230.

M. S. A. Mahmud, M. S. Zainal Abidin, Z. Mohamed, M. K. I Abd Rahman and I. Michihisa, Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment, Computers and Electronics in Agriculture, 157, 2019, 488-499.

Z. M. Hanafee, K. Abdan, M. Norkhairunnisa, Z. Edi Syams and K. E. Liew, The effect of different linear robot travel speed on mass flowrate of pineapple leaf fiber (PALF) automated spray up composite, Composite Part B: Engineering, 156, 2019, 220-228.

H. Z. Mohd, A. Khalina, M. Norkhairunnisa, S. Z. Edi, E. L. Kan and N. N. Mohd, Automated spray up process for Pineapple Leaf Fibre hybrid biocomposites, Composites Part B: Engineering, 177, 2019, 107306.

C. M. Jesus, M. B. G. Jose, A. Dionisio and R. Angela, Route planning for agricultural tasks: A general approach for fleets of autonomous vehicles in site-specific herbicide applications, Computers and Electronics in Agriculture, 127, 2016, 204-220.

Y. Liangliang, N. Noboru and T. Ryosuke, Development and application of a wheel-type robot tractor, Engineering in Agriculture, Environment and Food, 9(2), 2016, 131-140.

G. Zaidner and A. A. Shapiro, A novel data fusion algorithm for low-cost localization and navigation of autonomous vineyard sprayer robots, Biosystems Engineering (Advances in Robotic Agriculture for Crops), 146, 2016, 133-148.

I. Stefan, A. Aleksandr and D. Sinisa, Autonomous control for multi-agent non-uniform spraying, Applied Soft Computing, 80, 2019, 742-760.

S. F. Bruno, F. Heitor, H. G. Pedro, Y. M. Leandro, P. Gustavo, C. P. L. F. C. Andre, K. Bhaskar and U. Jo, An adaptive approach for UAV-based pesticide spraying in dynamic environments, Computers and Electronics in Agriculture, 138, 2017, 210-233.

X. Ya, P. Cheng, G. Lars, J. F. Pal and I. Volkan, Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper, Computers and Electronics in Agriculture, 157, 2019, 392-402.

D. P. Andreas, A. Jan and D. B. Josse, Development of a robot for harvesting strawberries, IFAC-PapersOnLine, 51(17), 2018, 14-19.

Y. Yang, Z. Kailiang, Y. Li and Z. Dongxing, Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN, Computers and Electronics in Agriculture, 163, 2019, 104864.

K. A. Farangis, G. V. Stavros and C. S. David, Development of a linear mixed model to predict the picking time in strawberry harvesting process, Biosystems Engineering, 166, 2018, 76-89.

J. Wei, Q. Zhijie, X. Bo, T. Yun, Z. Dean and D. Shihong, Apple tree branch segmentation from images with small gray level difference for agricultural harvesting robot, Optik, 127(23), 2016, 11173-11182.

B. Lingxin, H. Guangrui, C. Chengkun, S. Adilet and C. Jun, Experimental and simulation analysis of optimum picking patterns for robotic apple harvesting, Scientia Horticulture, 261, 2020, 108937.

W. Heng, J. H. Cameron, B. Santosh, K. Manoj, M. Changki and H. M. John, Simulation as a tool in designing and evaluating a robotic apple harvesting system, IFAC-PapersOnLine, 51(17), 2018, 135-140.

L. Jidong, W. Yijie, X. Liming, G. Yuwan, Z. Ling, Y. Biao and M. Zhenghua, A method to obtain the near-large fruit from apple image in orchard for single-arm apple harvesting robot, Scientia Horticulturae, 257, 2019, 108758.

N. Xindong, W. Xin, W. Shumao, W. Shubo, Y. Ze and M. Yibo, Structure design and image recognition research of a picking device on the apple picking robot, IFAC-PapersOnLine, 51(17), 2018, 489-494.

K. Hanwen and C. Chao, Fast implementation of real-time fruit detection in apple orchards using deep learning, Computers and Electronics in Agriculture, 168, 2019, 105108.

L. Zhiguo, M. Fengli, Y. Zhibo and W. Hongqiang, An anthropometric study for the anthropometric design of tomato-harvesting robots, Computers and Electronics in Agriculture, 163, 2019, 104881.

Z. Baohua, Z. Jun, M. Yimeng, Z. Na, G. Baoxing, Y. Zhenghong and I. Sunusi, Comparative study of mechanical damage caused by a two-finger tomato gripper with different robotic grasping patterns for harvesting robots, Biosystems Engineering, 171, 2018, 245-257.

O. Tomohiko, I. Yasunaga, N. Akimasa, K. Hiroki and H. Tadahisa, Development of yield and harvesting time monitoring system for tomato greenhouse production, Engineering in Agriculture, Environment and Food, 12(1), 2019, 40-47.

A. M. Henry Williams, H. Mark Jones, N. Mahla, J. Matthew Seabright, B. Jamie, D. Nicky Penhall, et al., Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms, Biosystems Engineering, 181, 2019, 140-156.

M. Longtao, C. Gongpei, L. Yadong, C. Yongjie, F. Longsheng and G. Yoshinori, Design and simulation of an integrated end-effector for picking kiwifruit by robot, Information Processing in Agriculture, 7(1), 2020, 58-71.

B. Ruud, H. Jochen and J. Van Henten Eldert, Angle estimation between plant parts for grasp optimization in harvest robots, Biosystems Engineering, 183, 2019, 26-46.

B. Ruud, H. Jochen and J. V. H Eldert, Design of an eye-in-hand sensing and servo control framework for harvesting robotics in dense vegetation, Biosystems Engineering, 146, 2016, 71-84.

L. Lufeng, T. Yunchao, L. Qinghua, C. Xiong, Z. Po and Z. Xiangjun, A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard, Computers in Industry, 99, 2018, 130-139.

Z. Jiajun, H. Chaojun, T. Yu, H. Yong, G. Qiwei, Z. Zhenyu and L. Shaoming, Computer vision-based localization of picking points for automatic litchi harvesting applications towards natural scenarios, Biosystems Engineering, 187, 2019, 1-20.

W. Yi, Y. Yan, Y. Changhui, Z. Hongmei, C. Gubo, Z. Zhe, et al., End-effector with a bite mode for harvesting citrus fruit in random stalk orientation environment. Computers and Electronics in Agriculture, 157, 2019, 454-470.

R. Ali and N. Noboru, Characterization of pumpkin for a harvesting robot, IFAC-PapersOnLine, 51(17), 2018, 23-30.

K. Tatsuki, R. Ali and N. Noboru, Heavy-weight crop harvesting robot-Controlling algorithm, IFAC-PapersOnLine, 51(17), 2018, 244-249.

Z. Yuanshen, G. Liang, H. Yixiang and L. Chengliang, A review of key techniques of vision-based control for harvesting robot, Computers and Electronics in Agriculture, 127, 2016, 311-323.

C. W. Bac, T. Roorda, R. Reshef, S. Berman, J. Hemming and E. J. van Henten, Analysis of a motion planning problem for sweet-pepper harvesting in a dense obstacle environment, Biosystems Engineering, 146, 2016, 85-97.

C. W. Bac, J. Hemming, B. van Tuijl, R. Barth, E. Wais and E. J. van Henten, Performance evaluation of a harvesting robot for sweet pepper, Journal of Field Robotics, 34(6), 2017, 1123-1139.

L. Mark and D. Amir, A conceptual framework and optimization for a task-based modular harvesting manipulator, Computers and Electronics in Agriculture, 166, 2019, 104987.

K. Gerard, I. Viorela and M. Robert, A perception pipeline for robotic harvesting of green asparagus, IFAC PapersOnLine, 52-30(2019), 2019, 288-293.

J. J. Zhuang, S. M. Luo, C. J. Huo, Y. Tang, Y. He and X. Y. Hue, Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications, Computers and Electronics in Agriculture, 152, 2018, 64-73.

L. Xiao, Z. Yuanshen, G. Liang, L. Chengliang and W. Tao, Dual-arm cooperation and implementing for robotic harvesting tomato using binocular vision, Robotics and Autonomous Systems, 114, 2019, 134-143.

G. Salvador, W. Alexander and U. James, Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation, Computers and Electronics in Agriculture, 164, 2019, 104890.

A. M. Hesham, E. S. Scot, L. R. Diane and A. E. R. Nader, Using multispectral imagery to extract a pure spectral canopy signature for predicting peanut maturity, Computers and Electronics in Agriculture, 162, 2019, 561-572.

G. M. Jordin, G. Eduard, G. Javier, A. Fernando, S. C. Ricardo, E. Alexandre, L. Jordi, J .R. Morros, R. H. Javier, V. Veronica and R. R. P. Joan, Fruit detection in an apple orchard using a mobile terrestrial laser scanner, Biosystems Engineering, 187, 2019, 171-184.

Z. Nurazwin, H. Norhashila, A. Khalina and H. Marsyita, Application of laser-induced backscattering imaging for predicting and classifying ripening stages of “Berangan” bananas, Computers and Electronics in Agriculture, 160, 2019, 100-107.

Agricultural Robots Market by Offering, Type (UAVs, Milking Robots, Driverless Tractors, Automated Harvesting Systems), Farming Environment, farm Produce, Application (Harvest Management, Field Farming, Geography - Global Forecast to 2025., 2020 (accessed 07.03.2020).

M. Vega-Heredia, R. E. Mohan, Y. W. Tan, J. S. Aisyah, A. Vengedesh, S. Ghanta and S. Vinu, Design and modelling of a modular window cleaning robot, Automation in Construction, 103, 2019, 268-278.

L. Barbieri, F. Bruno, A. Gallo, M. Muzzupappa and M. L. Russo, Design, prototyping and testing of a modular small-sized underwater robotic arm controlled through a master-slave approach, Ocean Engineering, 158, 2018, 253-262.

T. Milan, B. Adam and J. Slavka, Design of a prototype for a modular mobile robotic platform, IFAC-PapersOnLine, 52(27), 2019, 192-197.


  • There are currently no refbacks.