Njoro, KENYA
Hillary K. Bett: and
University website: Head, School of Agricultural Economics and Agribusiness Dire Dawa, ETHIOPIA Dr. Sime Shiferaw: University website: | Head, Department of Agricultural and Applied Economics Bunda, MALAWI Kennedy Machila: and University website: | Head, Department of Agribusiness and Natural Resource Economics Kampala, UGANDA Dr. Rosemary Isoto: University Website: |
Head, Department of Agricultural Economics and Agribusiness Morogoro, TANZANIA Dr. Fulgence Mishili: University Website: | Head, Department of Agricultural Economics Nairobi, KENYA Prof. Jonathan Nzuma: University website: |
Head, Department of Agricultural Economics, Extension & Rural Development Pretoria, SOUTH AFRICA Prof. Wale Edilegnaw Zegeye: University website: | Head, Department of Agricultural Business Development and Economics Harare, ZIMBABWE Edward Mutandwa and University website: |
Kwaluseni, eSwatini Dr. Sotja Graham Dlamini: University Website: | Stellenbosch, South Africa Prof. Andre Jooste: University Website: |
To qualify, candidates must:
- Have applied and been admitted to any one of the CMAAE-accredited (category B) universities listed above and obtained an admission letter;
- Have attained at least a Second-Class Honours (Upper Division) or equivalent in Agricultural Economics, Agribusiness management or related field from a recognized university; and
- Female and applicants from post-conflict and fragile states are highly encouraged to apply.
Applications for admission to the CMAAE programme should be sent directly to the degree-awarding universities whose website links and addresses are provided above.
Upon receipt of an admission letter, the scholarship application can be made. Visit https://training.aercafrica.org/
- BSc. certificate and transcripts
- Admission letter
- Cover letter
A total of 17 public universities in 13 Eastern, Central and Southern African countries participate in CMAAE. These universities are classified into two categories, category A and category B. Students in category A universities enroll for CMAAE studies in any of the 8 category B universities that are accredited to offer core courses and supervise theses research. The students in category B universities then jointly undertake elective courses at the Shared Facility for Specialization and Electives (SFSE) for a period of four months upon completion of their core courses. Apart from admitting and issuing degrees, the CMAAE non-accredited departments participate in all other programme activities including faculty research, faculty retooling, Academic Advisory Boards, and curriculum reviews, among others.
Collaborative Masters in Agricultural and Applied Economics (CMAAE) Participating Universities
Category A (Non-Accredited)
- Botswana University of Agriculture and Natural Resources
- Eduardo Mondlane University, Mozambique
- Jomo Kenyatta University of Agriculture and Technology, Kenya
- Moi University, Kenya
- University of Eswatini
- University of Juba, South Sudan
- University of Rwanda
- University of Stellenbosch, South Africa
- University of Zambia
Category B (Accredited)
- Egerton University, Kenya
- Haramaya University, Ethiopia
- Lilongwe University of Agriculture and Natural Resources (LUANAR), Malawi
- Makerere University, Uganda
- Sokoine University of Agriculture, Tanzania
- University of Nairobi, Kenya
- University of Pretoria, South Africa
- University of Zimbabwe
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Research progress on autonomous operation technology for agricultural equipment in large fields.
1. Introduction
2. onboard environmental sensing technology, 3. complete-coverage path-planning technology, 3.1. classical path-planning algorithm, 3.2. bionics-based path-planning algorithms, 4. autonomous operation control technology, 5. conclusions and prospection, 5.1. conclusions, 5.2. prospection, author contributions, data availability statement, conflicts of interest.
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Click here to enlarge figure
Method | Sensor Type | Characteristics | Sensing Task |
---|
Vision Sensors | Monocular Camera | Monocular cameras are low cost and provide rich image information, but lack depth data and are susceptible to environmental influences. | Farmland boundary detection, navigation line extraction |
Binocular Camera | Binocular cameras can provide rich image information and highly reliable depth information, but the configuration and calibration are more complicated; the computation is large, and parallax calculation depends on computing resources. | Farmland boundary detection, navigation line extraction |
RGB-D Camera | RGB-D camera can provide an RGB map and a depth map, and the calculation amount is small. However, the measurement range is narrow, the noise level is high, the field of view is small, and it is easily interfered with by daylight. | Farmland boundary detection |
Radar Sensor | Lidar | LIDAR is highly accurate, stable, and reliable. However, it has a high cost, is susceptible to dust interference with the limited detection range, and cannot recognize color and texture in farmland boundary identification and navigation line extraction. | Navigation line extraction |
Camera Type | Features | Advantages | Common Cameras |
---|
RGB Cameras | A standard color camera that captures images in the red, green, and blue color channels. | RGB cameras provide rich color and texture information that helps distinguish between different types of obstacles, are low cost, and are easy to integrate and deploy. | Logitech C920, Sony Alpha Series (Logitech, Lausanne, Switzerland) |
Depth Camera | In addition to capturing RGB images, it also acquires depth information for each pixel. | Combining depth information and RGB images improves the accuracy and reliability of obstacle detection, providing more precise obstacle localization, especially in complex environments. | Intel RealSense (Intel, Santa Clara, CA, USA), Microsoft Kinect 360 (Microsoft 360, Washington, DC, USA) |
Stereo Camera | Captures stereo images through two cameras and uses parallax to calculate depth information. | Provides high-precision depth perception for fine obstacle detection tasks and is more reliable than a single depth camera in terms of detection accuracy and range. | ZED Series (ZED Series, San Francisco, America), Bumblebee2 (Teledyne FLIR, Washington, DC, USA) |
Panoramic Camera | Capable of capturing images or videos with a 360-degree field of view. | In obstacle detection, it provides a comprehensive view of the environment, reduces blind spots, and improves the coverage and accuracy of obstacle detection. | Ricoh Theta (RICOH, Tōkyō, Japan), Insta360 Pro (insta360, Shenzhen, China) |
Classification | Common Algorithms | Common Application Areas |
---|
Algorithms based on graph search | Dijkstra, A *, D * | Global path planning |
Algorithm based on sampling | RRT | Global path planning |
Algorithms based on artificial potential fields | Artificial potential field method | Local path planning |
Algorithms based on curve fitting | Arcs and straight lines, polynomial curves, spline curves, Bessel curves, differential flatness | Local path planning |
Algorithms based on numerical optimization | Describing and solving planning problems using objective functions and constraints | Local path planning |
Intelligent algorithms based on bionics | Genetic algorithms, particle swarm optimization algorithms, ant colony algorithms | Global path planning, local path planning |
Step | GA | PSO | ACO |
---|
Initialization | Initialize population | Initialize particles | Initialize ants |
Fitness Eval. | Evaluate fitness | Evaluate fitness | Evaluate fitness |
Selection | Roulette wheel selection | N/A | Select next node based on probability |
Crossover | Single-point crossover | N/A | N/A |
Mutation | Swap mutation | N/A | N/A |
Update Ind. | Replace individual | Update velocity and position | Update pheromone |
Update Best | Find best individual | Update global best | Find global best path |
Iteration Loop | Repeat for max generations | Repeat for max iterations | Repeat for max iterations |
Return Result | Return best individual | Return global best | Return global best path |
Algorithm Category | Global Search Ability | Convergence Speed | Computational Complexity | Adaptability | Scalability |
---|
Genetic algorithm | ★★★★ | ★★ | ★★★★ | ★★★★ | ★★★ |
Particle swarm optimization | ★★★★ | ★★★★★ | ★★★ | ★★★ | ★★★★ |
Ant colony algorithm | ★★★★★ | ★★★ | ★★★★ | ★★★★ | ★★★★ |
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Share and Cite
Wei, W.; Xiao, M.; Duan, W.; Wang, H.; Zhu, Y.; Zhai, C.; Geng, G. Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields. Agriculture 2024 , 14 , 1473. https://doi.org/10.3390/agriculture14091473
Wei W, Xiao M, Duan W, Wang H, Zhu Y, Zhai C, Geng G. Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields. Agriculture . 2024; 14(9):1473. https://doi.org/10.3390/agriculture14091473
Wei, Wenbo, Maohua Xiao, Weiwei Duan, Hui Wang, Yejun Zhu, Cheng Zhai, and Guosheng Geng. 2024. "Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields" Agriculture 14, no. 9: 1473. https://doi.org/10.3390/agriculture14091473
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Agriculture is a labor-intensive industry. However, with the demographic shift toward an aging population, agriculture is increasingly confronted with a labor shortage. The technology for autonomous operation of agricultural equipment in large fields can improve productivity and reduce labor intensity, which can help alleviate the impact of population aging on agriculture.