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      Cassava breeding and agronomy in Asia: 50 years of history and future directions

      research-article
      1 , * , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 3 , 9 , 4 , 2 , 10 , 8 , 11 , 8 , 2 , 8 , 4 , 12 , 13 , 1 , 3 , 9 , 3 , 9 , 4 , 4 , 3 , 3 , 3 , 3 , 8 , *
      Breeding Science
      Japanese Society of Breeding
      Asia, cassava, conventional breeding, agronomy, new breeding techniques, data-driven agriculture, CMD

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          Abstract

          In Asia, cassava ( Manihot esculenta) is cultivated by more than 8 million farmers, driving the rural economy of many countries. The International Center for Tropical Agriculture (CIAT), in partnership with national agricultural research institutes (NARIs), instigated breeding and agronomic research in Asia, 1983. The breeding program has successfully released high-yielding cultivars resulting in an average yield increase from 13.0 t ha –1 in 1996 to 21.3 t ha –1 in 2016, with significant economic benefits. Following the success in increasing yields, cassava breeding has turned its focus to higher-value traits, such as waxy cassava, to reach new market niches. More recently, building resistance to invasive pests and diseases has become a top priority due to the emergent threat of cassava mosaic disease (CMD). The agronomic research involves driving profitability with advanced technologies focusing on better agronomic management practices thereby maintaining sustainable production systems. Remote sensing technologies are being tested for trait discovery and large-scale field evaluation of cassava. In summary, cassava breeding in Asia is driven by a combination of food and market demand with technological innovations to increase the productivity. Further, exploration in the potential of data-driven agriculture is needed to empower researchers and producers for sustainable advancement.

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          Most cited references108

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          Machine Learning in Agriculture: A Review

          Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.
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            Translating High-Throughput Phenotyping into Genetic Gain

            Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and ‘phenotypers’ in an effective manner.
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              An explainable deep machine vision framework for plant stress phenotyping

              Significance Plant stress identification based on visual symptoms has predominately remained a manual exercise performed by trained pathologists, primarily due to the occurrence of confounding symptoms. However, the manual rating process is tedious, is time-consuming, and suffers from inter- and intrarater variabilities. Our work resolves such issues via the concept of explainable deep machine learning to automate the process of plant stress identification, classification, and quantification. We construct a very accurate model that can not only deliver trained pathologist-level performance but can also explain which visual symptoms are used to make predictions. We demonstrate that our method is applicable to a large variety of biotic and abiotic stresses and is transferable to other imaging conditions and plants.
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                Author and article information

                Journal
                Breed Sci
                Breed. Sci
                jsbbs
                Breeding Science
                Japanese Society of Breeding
                1344-7610
                1347-3735
                April 2020
                5 March 2020
                : 70
                : 2
                : 145-166
                Affiliations
                [1 ] International Center for Tropical Agriculture (CIAT-Laos), Lao PDR Office , Dong Dok, Ban Nongviengkham, Vientiane, Lao PDR
                [2 ] Department of Agronomy, Faculty of Agriculture, Kasetsart University , 50 Ngam Wong Wan Rd, Chatuchak Bangkok 10900, Thailand
                [3 ] International Laboratory for Cassava Molecular Breeding, National Key Laboratory for Plant Cell Biotechnology, Agricultural Genetics Institute , Pham Van Dong Rd, Bac Tu Liem District, Hanoi, Vietnam
                [4 ] Chinese Academy of Tropical Agricultural Sciences (CATAS) , 571737, Hainan Province, the People’s Republic of China
                [5 ] Indonesian Legume and Tuber Crops Research Institute , Kendalpayak Km 8, PO BOX 66, Malang 65101, Indonesia
                [6 ] Faculty of Agriculture & Food Processing, University of Battambang , Battambang, Cambodia
                [7 ] Central Tuber Crops Research Institute Sreekariyam , Thiruvananthapuram-605 017, Kerala, India
                [8 ] International Center for Tropical Agriculture (CIAT) , Km 17, Recta Cali-Palmira Apartado Aéreo 6713, Cali, Colombia
                [9 ] RIKEN Center for Sustainable Resource Science , 1-7-22 Suehiro-cho, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
                [10 ] Hung Loc Agricultural Research Center, Institute for Agriculture in Southern Vietnam , 121 Nguyen Binh Khiem, District 1, HCM City, Vietnam
                [11 ] Root and Tuber Crop Research and Development Center, Food and Field Crop Research Institute , Vinh Quynh, Thanh Tri, Hanoi, Vietnam
                [12 ] Rayong Field Crops Research Center , Sukumvit Rd, Huaypong, Meang, Rayong 21150, Thailand
                [13 ] International Center for Tropical Agriculture (CIAT-Asia) , Phnom Penh, Cambodia
                Author notes
                [* ]Corresponding authors (e-mail: a.malik@ 123456cgiar.org and m.ishitani@ 123456cgiar.org )

                Communicated by Norihiko Tomooka

                Article
                JST.JSTAGE/jsbbs/18180 18180
                10.1270/jsbbs.18180
                7272245
                975a7081-6137-4f8f-abf9-94b95c7b7e29
                Copyright © 2020 by JAPANESE SOCIETY OF BREEDING

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 November 2018
                : 29 September 2019
                Categories
                Review

                Animal agriculture
                asia,cassava,conventional breeding,agronomy,new breeding techniques,data-driven agriculture,cmd

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