International Journal of Sustainable Agricultural Management and Informatics (5 papers in press)

Regular Issues

• An innovative artificial intelligence approach for disease classification in plants  
  by Nitin Vamsi Dantu, Shriram K. Vasudevan, K. Vimalkumar 
  Abstract: The farmers are facing a lot of challenges and one of the main problems they face is because of plant diseases. The energy to the disease fungi is taken from the plants which they live on. They are responsible for the huge damage and the damages are classified into wilting, rusts, blotches, scabs, mouldy coatings, and rotted tissue. Most of the farmers are being affected with huge losses as they do not find the right solution for a certain disease that their crops have. The primary goal of this research is to find the common diseases in the plants and suggest the optimal solution that helps in reducing the fault rate of crops and in turn, it increases the crop yield. This would lower the crop damage drastically and the consumers can purchase better quality products. We propose to use deep learning techniques to identify diseases in crops in real-time, the same can be made as a mobile app that could help farmers or anyone to detect the diseases for the plants. The system is found to be functionally very stable and works under all ideal conditions.
  Keywords: plant diseases; image processing; convolution neural network; CNN; classification; technology for agriculture; deep learning.
  DOI: 10.1504/IJSAMI.2020.10031701
   
  
• Does farm size matters in determining efficiency in Indian agriculture: a case study of rice production in Eastern India  
  by Chandrima Chakraborty, Dipyaman Pal 
  Abstract: The present paper analyses the relationship between farm size and technical efficiency of Indian agricultural sector based on primary data from the Ballavpur village of West Midnapore district in West Bengal for the period 2017-2018. The analysis concludes that the output efficiency increases with the increase in farm size. The major factor behind the variation in output efficiency is the area under irrigation. Also, farmers' literacy rate may raise the efficiency level of their farms. Besides this, if a farmer wants to increase the efficiency level then the cost of transportation and the rent paid must have to be decreased. There is also a scope of improvement in the efficiency level for all categories of farmers by the improvement in the irrigation facility.
  Keywords: Indian agriculture; output efficiency; farm size; data envelopment analysis; DEA; multinomial probit; India.
  DOI: 10.1504/IJSAMI.2020.10034419
   
  
• Drylands rainwater harvesting: a community-based management approach  
  by Mohammad N. Alhamad, Mohammad A. Alrababah, Samah A. Jaradat, Anne A. Gharaibeh, Shorna B. Allred 
  Abstract: This study's objectives were to identify issues concerning natural resource use and identify management options for natural resources from the local community's perspective. The research methodology is based on an integrated application of field interviews with individuals, focus groups, and feedback from key informants. The results showed that less than 1% of the local community supports the construction of Earth dams on rangeland reserves. Land donation and sharing by the landowners is a possible proposal for selecting potential sites for Earth dams. The lack of active participation of local communities during the planning and execution of natural resources development projects led to project failure to secure water resources in the study area. We recommend policymakers and water resources managers seriously consider active community involvement in water harvesting programs. Hence, the present top-bottom approach should be replaced by the bottom-up approach to provide sustainable assurances to water development programs.
  Keywords: water harvesting; local knowledge; Mediterranean dryland; bottom-up approach; sustainable management; arid land; Earth dams; rangeland resources; water resources; Jordan.
  DOI: 10.1504/IJSAMI.2021.10035911
   
  
• A mobile application as a supporting tool for weight equalisation in animal experiments  
  by Oluwasefunmi 'Tale Arogundade, Olufemi Sunday Akinola, Motunrayo Balogun, Adebayo A. Abayomi-Alli, Abiodun Motunrayo Ikotun, Ibukun Grace Fadahunsi, Temitope Elizabeth Abioye 
  Abstract: Weight equalisation (WE) is the technique of grouping experimental subjects with the aim of reducing the variability in the starting weight among experiment treatment groups. Existing methods are cumbersome, especially when dealing with large number of animals. This work developed a supporting tool for WE called WETool. It was implemented as an Android mobile application adopting quick sorting technique, Z score outlier detection with sinusoidal arrangement as the method for partitioning and sorting the experimental subjects into groups. Experimental data of 24 subjects were sorted using random sorting and WETool into six groups of four experimental subjects per group. Assigning the subjects resulted in group minimum weight that ranged from 13.75-10.125 and 12.5-12; with a difference of 3.625 and 0.5, per group; variance of 1.915 and 0.046 for random sorting and WETool respectively. The results of the acceptability evaluation of WETool were also very promising.
  Keywords: animal experimentation; weight equalisation; experimental subjects; mobile app; treatment; data; WETool; sorting; tool; groups.
  DOI: 10.1504/IJSAMI.2021.10035209
   
  
• Soil utilisation prediction for farmers using machine learning  
  by Abdul Qadir Zakir, Anushka Singhal, Gurkirat Singh, Pracheesh Pandey, Suresh Sankaranarayanan 
  Abstract: Soil is necessary for the growth of the plant and there is a need to know the plant that can be grown. Methods are used for soil analysis by taking soil samples in the lab. Soil indicators analyse the soil remotely from the field. The literature review indicates that no work has been done using machine learning for analysing the soil features for soil utility prediction. We have done an in-depth soil utility analysis by employing deep learning and comparing it with other machine learning models for soil utility prediction resulting in the best prediction model.
  Keywords: soil sample; soil analysis; soil utility; machine learning; deep learning.
  DOI: 10.1504/IJSAMI.2021.10035347