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1. Spectral assessment of chickpea morpho-physiological traits from space, air and groundChickpea (Cicer arietinum) is an important grain legume in semi-arid regions and water-stress is a major constraint to its productivity. Area under chickpea cultivation is growing but climate change toward greater aridity results in higher precipitation instability and risks yields. The ability to assess water potential can support irrigation decisions. Thus, improved ability to spatially assess plants water status can promote more efficient irrigation. The current... I. Herrmann, R. Sadeh, A. Avneri, Y. Tubul, R. Lati, S. Abbo, D.J. Bonfil, Z. Peleg |
2. Evaluation of on –farm oil palm yield parameters in Niger Delta region of NigeriaEvaluation of on –farm oil palm yield parameters in Niger Delta region of Nigeria *Ekhator F1., Osayande P1., Aduramigba-Modupe V.O2., Solomon O1., and Ikuenobe C.E1. 1Nigerian Institute for Oil Palm Research (NIFOR) P.M.B 1030, Benin City, Edo State 2Institute of Agricultural Research and Training, Obafemi Awolowo University, Ibadan, Oyo... E.C. Ikuenobe, K.D. Madukwe, F. Ekhator, V.M. Aduramigba, P.E. Osayande, S. Solomon, N.M. Okoye |
3. Development of Canopy Mapping System of Asian pears (Pyrus pyrifolia Naka) Using Terrestrial Laser ScanningIn this paper, the canopy mapping system (CMS) of Asian pears for estimating yield during Bud thinning and Pruning operations using point cloud data was proposed. Bud thinning and Pruning in Asian pear (Pyrus pyrifolia Naka) is necessary to ensure quality and yield but is time-consuming and heavily depends on work knowledge. This study described a method of estimating the number of fruits through the length of a branch based on remote sensing. The CMS would be useful to support more efficient... E. Morimoto, J. Lee, K. Nonami, I. Matumura, M. Ikebe, S. Sato |
4. Development of Lodging Direction Determination System Using Image ProcessingIn this study, image processing system was developed for application on rice plants to determine lodging condition, which was contributing factor to declining harvester efficiency by using combine harvester. Therefore, We developed a system for determination of the lodging direction by algorithm based on convolutional neural network (CNN). As for deep learning framework, Pytorch1.1.0 were used to train and test the judging direction. GoogLeNet was used as a pre-trained CNN model. Lodging... E. Morimoto, Y. Arai, K. Nonami, T. Ito |