International Journal of Agriculture Innovation, Technology and Globalisation (6 papers in press)

Regular Issues

• Review: anti-influenza viral effects of camellia tea  
  by P.G. Tharindu, I.D.U.H. Piyathilake, E.P.N. Udayakumara 
  Abstract: Influenza virus cause 35 million severe medical cases, including nearly 500,000 respiratory deaths globally annually. Even with vaccines and antiviral drugs, it always had managed reemerge. In traditional medicine, Camellia sinensis tea is used as a remedy and prevention against Influenza. This article researched previous literature rigorously to analyse the antiinfluenza viral effects of camellia tea, experimental designs, gaps in previous work and limitations of employed approaches. In literature, epigallocatechin gallate (EGCG) and theaflavin digallate (TF3) in camellia tea had shown prominent anti-viral properties against influenza. Clinical approach, in-vitro and social survey were commonly used approaches while majority were clinical approaches. Surveys were least used. The accuracy of the in-vitro was highest, while social surveys being least accurate. We further noticed that the exact mechanisms, pharmacokinetics, pharmacodynamics and toxicology of these compounds as independent or as mixtures in the real human body is not yet fully known.
  Keywords: influenza; camellia tea; anti-viral; biochemical; clinical.
  DOI: 10.1504/IJAITG.2022.10051319
   
  
• Towards national-size digital platform and ecosystem of smart services for precision farming  
  by Oleg Goryanin, Petr Skobelev, Elena Simonova, Aleksey Tabachinskiy 
  Abstract: The paper describes problem statement and developed prototype of Smart Farming solution, which is designed as an open digital platform and ecosystem (system of systems) of smart agricultural services. Each service is implemented as an autonomous artificial intelligence system for decision-making support. The paper describes the functionality of the created prototypes of services within the Smart Farming solution for precision farming and the possibilities of their use for a wider range of consumers, including state authorities, universities, suppliers of fertilisers and plant protection products, technology companies, startups and end users. A distinctive feature of the considered system is the use of knowledge bases and multi-agent technologies in order to support collective decision-making processes regarding allocation, planning, optimisation and control of resources of farms in real time. Furthermore, the paper discusses results of development and first applications, as well as directions for further development of the proposed system.
  Keywords: Industry 5.0; Society 5.0; precision farming; digital ecosystem; multi-agent technologies; knowledge base; decision making; resource management; digital twin of plant.
  DOI: 10.1504/IJAITG.2023.10055138
   
  
• The key factors consumer purchasing behaviour toward soybean in Thailand  
  by Tzong-Ru Lee, Yong-Shun Lin, Wilaiporn Paisarn, Natchaya Kraipuy, Guang-Jing Chen, Shaw-Yhi Hwang 
  Abstract: The soybean is suggested in many countries because of its nutrients. Especially in Thailand, the consumption surges dramatically. The soybean market is recommended as the potential market for the marketer. This research will assist those by discovering the determinants of consumer purchasing behaviour toward soybean in Thailand. The questionnaires were collected through Google Form from 218 respondents who were soybean consumers. The research provided 21 factors that consumers might consider before purchasing soybean. The data was analysed by grey relation analysis (GRA). The consumer purchasing behaviour toward soybean was revealed in this research. The results showed that the key factors of consumer purchasing behaviour toward soybean were quality, product expiration date, health effects, taste, and nutrition respectively.
  Keywords: consumer decision; GRA degree; soybean; Thailand.
  DOI: 10.1504/IJAITG.2023.10056559
   
  
• Extreme learning machine for solving paddy nutrient deficiencies in Davangere region  
  by M. Varsha, M.P. Pavan Kumar, S. Basavarajappa 
  Abstract: Soil nutrient is an important aspect that contributes to the soil fertility and environmental effects. Traditional evaluation approaches of soil nutrient are quite hard to operate and they are very slow, making great difficulties in practical applications. The proposed study, presents extreme learning machine (ELM) for analysing soil fertility index values of boron, zinc, organic carbon and pH in Davangere District. Boron, zinc, organic carbon and pH concentrations in soil plays as significant factors for paddy crop cultivations and growth. Proposed ELM-based approach helps in prediction of boron, zinc, organic carbon and pH index values in soil by evaluating four linear and nonlinear activations functions. Performance of ELM model is analysed by increasing number of hidden neurons in the hidden layer.
  Keywords: extreme learning machine; ELM; transfer functions; hidden neurons; back-propagation.
  DOI: 10.1504/IJAITG.2023.10057454
   
  
• Machine learning-based approach for degree of milling analysis of Indian rice variety  
  by S. Harini, Saritha Chakrasali, G.N. Krishnamurthy 
  Abstract: Image processing and machine learning has a wide application in the field of agriculture and food industry. This is because of the non-destructive evaluation process, performance and low cost compared to manual methods. Analysing grain quality manually is laborious and also subjective. It totally depends on the knowledge and experience of the experts. Rice is one of the staple food grains in major countries of the world. India being one of the top most exporters of rice grains, the quality analysis is very crucial. The food industry and consumers suffer from the lack of fast, automated solution for identifying the quality of grains. To address this problem, this work proposes a machine learning-based solution for automatic analysis of quality of rice grains using degree of milling (DOM). Various machine learning algorithms are used for the analysis. A noticeable result is obtained for SVM, KNN, decision tree and CNN algorithms with an accuracy of 96%, 90%, 88% and 100% accuracy, respectively.
  Keywords: convolution neural network; CNN; SVM; decision tree; KNN; Indian rice; degree of milling; DOM.
  DOI: 10.1504/IJAITG.2023.10058269
   
  
• Sustainable Bio-based Planting Pots as an Approach to Reduce Plastic Waste in the Agriculture Industry  
  by Anunay Gupta, Nolan Urahn, Arup Dey, Nita Yodo, David Grewell, Chiwon W. Lee 
  Abstract: Because of the non-biodegradable nature and the associated environmental damage of plastic pots, there is a need to find an alternative that is more environmentally friendly. In the agriculture industry, planting pots made from bio-based materials can potentially replace traditional plastic pots and provide additional health benefits to plants. This paper reviews the current advancement of bio-based planting pots. Various plant pots available in the market are summarised, focusing on their advantages and limitations. Plant health assessments of specimens grown in bio-based pots and their biodegradability are reviewed and compared with the traditional plastic pots. In addition, the techno-economic evaluation of bio-based and traditional plastic pots is summarised. The customer perception of these bio-based pots was also investigated for a widespread shift towards adopting bio-based plant pots. This literature review aims to help researchers and practitioners develop environmental-friendly bio-based agriculture products by identifying implementation gaps and future research directions.
  Keywords: bioplastics; pots; plant health; agriculture; horticulture; sustainable.
  DOI: 10.1504/IJAITG.2023.10059142