Proceedings
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| Filter results7 paper(s) found. |
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1. Early Detection of Oil Palm Fungal Disease Infestation Using A Mid-Infrared Spectroscopy TechniqueBasal stem rot (BSR) caused by Ganoderma boninense is known as the most destructive disease of oil palm plantations in Southeast Asia. Ganoderma could potentially reduce the market share of palm oil for Malaysia. Currently Malaysia produces about 50% of the world’s supply of palm oil. Early, accurate, and non-destructive diagnosis of Ganoderma fungal infection is critical for management of this disease. Early disease management of Ganoderma could also prevent great losses in production and... S. Liaghat, S. Mansor, H. Shafri, S. Meon, R. Ehsani, S. Azam, N. Noh |
2. A Non-Destructive Method of Estimating Red Tip Disease in PineappleRed Tip disease typically reduces pineapple yields by up to 50%. At present, the causal agent of Red Tip disease is still unconfirmed. B... F. Abu kassim, G. Vadamalai, A. Mohd hanif, S.K. Balasundram |
3. Detection Of Fruit In Canopy Night-Time Images: Two Case Studies With Apple And MangoReliable estimation of the expected yield remains a major challenge in orchards. In a recent work we reported the development of an algorithm for estimating the number of fruits in images of apple trees acquired in natural daylight conditions. In the present work we tested this approach with night-time images of similar apple trees and further adapted this approach to night-time images of mango trees. Working with the apple images required only... R. Linker, A. Payne, K. Walsh, O. Cohen |
4. Using Precision Agriculture And Remote Sensing Techniques To Improve Genotype Selection In A Breeding ProgramPrecision Agriculture (PA) and Remote Sensing (RS) technologies are increasingly being used as tools to assess crop and soil properties by breeders and physiologists. These technologies are showing potential to improve genotype selections over their traditional field measurements, by providing quick access to crop properties throughout the crop cycle and yield estimation. The objective of this work was to use vegetation indices (VIs) and soil apparent electrical conductivity... F.A. Rodrigues junior, I. Ortiz-monasterio, P.J. Zarco-tejada, K. Ammar, B.G. Gérard |
5. High Resolution Hyperspectral Imagery to Assess Wheat Grain Protein in a Farmer's FieldThe agricultural research sector is working to develop new technologies and management knowledge to sustainably increase food productivity, to ensure global food security and decrease poverty. Wheat is one of the most important crops into this scenario, being among the three most important cereal commodities produced worldwide. Precision Agriculture (PA) and specially Remote Sensing (RS) technologies have become in the recent years more affordable which has improved the availability and flexibility... F.A. Rodrigues jr., I. Ortiz-monasterio, P.J. Zarco-tejada, F.H. Toledo, U. Schulthess, B. Gérard |
6. Investigating the Potential of Visible and Near-infrared Spectroscopy (VNIR) for Detecting Phosphorus Status of Winter Wheat Leaves Grown in Long-term TrialThe determination of plant nutrient content is crucial for evaluating crop nutrient removal, enhancing nutrient use efficiency, and optimizing yields. Nutrient conventional monitoring involves colorimetric analyses in the laboratory; however, this approach is labor-intensive, costly, and time-consuming. The visible and near-infrared spectroscopy (VNIR) or hyperspectral non-imaging sensors have been an emerging technology that has been proved its potential for rapid detection of plant nutrient... Y. El-mejjaouy, B. Dumont, A. Oukarroum, B. Mercatoris , P. Vermeulen |
7. Data-driven Agriculture and Sustainable Farming: Friends or Foes?Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies... O. Rozenstein, Y. Cohen, V. Alchanatis , K. Behrendt, D.J. Bonfil, G. Eshel, A. Harari, W.E. Harris, I. Klapp, Y. Laor, R. Linker, T. Paz-kagan, S. Peets, M.S. Rutter, Y. Salzer, J. Lowenberg-deboer |