Browsing by Author "Ocen, Gilbert Gilibrays"

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    Agriculture 4.0: The Promises for Sustainable Agricultural and Food Systems
    (Busitema University, 2021-01) Bongomin, Ocident; Okello, Collins; Ocen, Gilbert Gilibrays; Tigalana, Dan
    The transformative power of industry 4.0 in agricultural and food systems (Agri-food) can be attested from the explosive disruption of agricultural production infrastructures such as connected farms, new farm equipment, and connected tractors and machines which is well-known today as Agriculture 4.0 or Agri-food 4.0. The driving force behind the emergence of Agriculture 4.0 is the dire need to increase efficiency, productivity and quality in agri-food systems, and environmental protection. This has gained attention of many researchers in the recent past and thus, making Agriculture 4.0 a buzzword among the academic literature today. Despite the fact that a number of studies have covered the applications of several disruptive technologies in agri-food, the key technologies that are transforming the agri-food have been ill-defined. Therefore, the present paper aimed at identifying the key disruptive technologies and highlighting their application areas in agri-food. Massive exploratory literature search was conducted on the published papers obtained from the electronic databases including Scopus, ScienceDirect, Wiley, Emerald insight, Taylor & Francis, and Springer. The applications of 11 disruptive technologies in agri-food were analyzed based on 119 published papers. The results showed that 5 key disruptive technologies including Internet of things, Drones, Blockchain, Big Data, and Robotics are emblematic of Agriculture 4.0 epoch. The application areas of these technologies in agri-food are clearly highlighted. The present study revealed the need for extensive research to expand the application areas of the disruptive technologies in agri-food.
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    Applications of Drones and Image Analytics in Field Phenotyping: A Potential Breakthrough in Uganda’s Agricultural Research
    (SSRN, 2022) Bongomin, Ocident; Lamo, Jimmy; Guina, Joshua Mugeziaubwa; Okello, ,Collins; Ocen, Gilbert Gilibrays; Obura, Morish; Alibu, Simon; Owino, Cynthia Awuor; Akwero, Agnes; Ojok, Samson
    We are in the race against time to find new solutions amidst the threat of climate change, to increase food production by 70% to feed the ever-growing world population which is expected to double by 2050. Agricultural research plays astonishing roles in crop and livestock improvement through breeding programs and good agronomic practices to enable sustainable agriculture and food systems. The advanced molecular breeding or modern breeding technologies in genotyping have been well-embraced by most research institutions worldwide. However, phenotyping which plays great role in agricultural research and breeding programs has achieved little development or still a traditional method in most institutions across African countries. Noteworthy, the advancement of phenotyping has been gaining momentum and attracted a number of researchers in the recent past, this led to the coining of high-throughput phenotyping concept. Nevertheless, the comprehensive understanding of this concept remains limited in most research institutions in developing countries, especially Uganda. Therefore, the present review aimed to provide a summary of drone-based high throughput phenotyping used across different crops. The electronic literature search was conducted from non-academic and academic databases. The literature sources in the form of peer-reviewed journal articles, books, book sections, conference papers, thesis and dissertations, policy papers, organisation or company manuals, working papers, and reports were considered. In this review, the concepts of field phenotyping are discussed, drone classification and specifications are elaborated, the use cases of the drone-based high-throughput phenotyping are presented, drone imaging systems for phenotyping are discussed, and high-throughput image analytics method is explained. In this paper, it was found that cereals have been the most studied crop for drone based phenotyping application in academic literature. However, root crops were the list studied, hence, extensive research is needed for drone-based phenotyping adoption in root crops. Moreover, limited studies have been focused on the effect of drones’ operation parameters. Therefore, research focusing on the optimization of the drones’ performance is required.