Image-Based Solutions for Precision Food Loss Evaluation
DOI:
https://doi.org/10.7160/aol.2024.160403How to Cite
Abstract
The high amount of food loss and waste significantly challenges the sustainable development. The agriculture needs rapid and fundamental transformation to enhance its efficient and sustainabile operation. However, to measure precisely the effect of the new policies and practices is also difficult. The present study analyses the applied methods’ data sources, as one of the key factors regarding the effective estimation of food loss and waste. By conducting a systematic literature review using the PRISMA approach, a lack of scientific focus was found related to the new data collection methods. Based on the selected articles reasonably slight amount joined the application of image processing to food loss estimation related purposes. The reviewed studies principally used the image-based solutions for the prevention and reduction of on-farm food loss. This recognition lighted up the application of image processing in agriculture, but only the thematic map analysis revealed the privileged status of ”plant disease detection” within the studied area. The results suggest the possibility of applying image-based data sources to quantify food loss. Even though the limitations of agricultural image processing, the application of new data sources, and methods could considerably improve the accuracy of food loss and waste quantification in addition to the operation on farm level in short term.References
Abbade, E. B. (2020) "Land and water footprints associated with rice and maize losses in Brazil", Land Use Policy, Vol. 99, p. 105106. ISSN 0264-8377. DOI: https://doi.org/10.1016/j.landusepol.2020.105106
Abbade, E. B. (2023) "Land footprint and GHG emissions from global food loss", Journal of the Science of Food and Agriculture, Vol. 103, No. 9, pp. 4430–4440. ISSN 1097-0010. DOI: https://doi.org/10.1002/jsfa.12524
Agbo, F. J., Oyelere, S. S., Suhonen, J. and Tukiainen, M. (2021) "Scientific production and thematic breakthroughs in smart learning environments: a bibliometric analysis", Smart Learning Environments, Vol. 8, No. 1, p. 1. ISSN 2196-7091. DOI: https://doi.org/10.1186/s40561-020-00145-4
Alzubi, J., Nayyar, A. and Kumar, A. (2018) "Machine Learning from Theory to Algorithms: An Overview", Journal of Physics: Conference Series", Vol. 1142, p. 012012. ISSN 1742-6596. DOI: https://doi.org/10.1088/1742-6596/1142/1/012012
Anriquez, G., Foster, W., Ortega, J. and Santos Rocha, J. (2021) "In search of economically significant food losses: Evidence from Tunisia and Egypt", Food Policy, Vol. 98, p. 101912. ISSN 0306-9192. DOI: https://doi.org/10.1016/j.foodpol.2020.101912
Aria, M. and Cuccurullo, C. (2017) "bibliometrix: An R-tool for comprehensive science mapping analysis", Journal of Informetrics, Vol. 11, No. 4, pp. 959-975. ISSN 1751-1577. DOI: https://doi.org/10.1016/j.joi.2017.08.007
Assunção, E. T., Gaspar, P. D., Mesquita, R. J. M., Simões, M. P., Ramos, A., Proença, H. and Inacio, P. R. M. (2022) "Peaches Detection Using a Deep Learning Technique—A Contribution to Yield Estimation, Resources Management, and Circular Economy", Climate, Vol. 10, No. 2, p. 11. ISSN 2225-1154. DOI: https://doi.org/10.3390/cli10020011
Boiteau, J. M. and Pingali, P. (2023) "Can we agree on a food loss and waste definition? An assessment of definitional elements for a globally applicable framework", Global Food Security, Vol. 37, p. 100677. ISSN 2211-9124. DOI: https://doi.org/10.1016/j.gfs.2023.100677
Capone, R., Berjan, S., El Bilali, H., Debs, P. and Allahyari, M. S. (2020) "Environmental implications of global food loss and waste with a glimpse on the Mediterranean region", International Food Research Journal, Vol. 27, No. 6. ISSN 1985-4668.
Chaboud, G. and Daviron, B. (2017) "Food losses and waste: Navigating the inconsistencies", Global Food Security, Vol. 12, pp. 1-7. ISSN 2211-9124. DOI: https://doi.org/10.1016/j.gfs.2016.11.004
Chauhan, C., Dhir, A., Akram, M. U. and Salo, J. (2021) "Food loss and waste in food supply chains. A systematic literature review and framework development approach", Journal of Cleaner Production", Vol. 295, p. 126438. ISSN 0959-6526. DOI: https://doi.org/10.1016/j.jclepro.2021.126438
Corrado, S. and Sala, S. (2018) "Food waste accounting along global and European food supply chains: State of the art and outlook", Waste Management, Vol. 79, pp. 120-131. ISSN 0956-053X. DOI: https://doi.org/10.1016/j.wasman.2018.07.032
Dal’ Magro, G. P. and Talamini, E. (2019) "Estimating the magnitude of the food loss and waste generated in Brazil", Waste Management & Research, Vol. 37, No. 7, pp. 706-716. ISSN 0734-242X. DOI: https://doi.org/10.1177/0734242X19836710
Delgado, L., Schuster, M. and Torero, M. (2017) "Reality of food losses: A new measurement methodology", [Online]. Available: https://www.oneplanetnetwork.org/sites/default/files/the_reality_of_food_losses_a_new_measurement_methodology.pdf [Accessed: Dec. 2, 2023].
Delgado, L., Schuster, M. and Torero, M. (2021) "Quantity and quality food losses across the value Chain: A Comparative analysis", Food Policy, Vol. 98, p. 101958. ISSN 0306-9192. DOI: https://doi.org/10.1016/j.foodpol.2020.101958
Ellison, B., Muth, M.K. and Golan, E. (2019) "Opportunities and Challenges in Conducting Economic Research on Food Loss and Waste", Applied Economic Perspectives and Policy, Vol. 41, No. 1, pp. 1-19. ISSN 2040-5804. DOI: https://doi.org/10.1093/aepp/ppy035
FAO (2011) "Global food losses and food waste – Extent, causes and prevention", Food and Agricultural Organisation of the United Nations. ISBN 978-92-5-107205-9.
FAO (2019) "The state of food and agriculture 2019. Moving forward on food loss and waste reduction", FAO, Rome, pp. 2-13. ISBN 978-92-5-131789-1.
Garcia-Herrero, I., Hoehn, D., Margallo, M., Laso, J., Bala, A., Batlle-Bayer, L., Fullana, P., Vazquez-Rowe, I., Gonzalez, M. J., Durá, M. J., Sarabia, C., Abajas, R., Amo-Setien, F. J., Quiñones, A., Irabien, A. and Aldaco, R. (2018) "On the estimation of potential food waste reduction to support sustainable production and consumption policies", Food Policy, Vol. 80, pp. 24-38. ISSN 0306-9192. DOI: https://doi.org/10.1016/j.foodpol.2018.08.007
Guo, X., Broeze, J., Groot, J. J., Axmann, H. and Vollebregt, M. (2020) "A Worldwide Hotspot Analysis on Food Loss and Waste, Associated Greenhouse Gas Emissions, and Protein Losses", Sustainability, Vol. 12, No. 18, p. 7488. ISSN 2071-1050. DOI: https://doi.org/10.3390/su12187488
Hall, O. and Wahab, I. (2021) "The Use of Drones in the Spatial Social Sciences", Drones, Vol. 5, No. 4, p. 112. ISSN 2504-446X. DOI: https://doi.org/10.3390/drones5040112
Hartikainen, H., Mogensen, L., Svanes, E. and Franke, U. (2018) "Food waste quantification in primary production – The Nordic countries as a case study", Waste Management, Vol. 71, pp. 502-511. ISSN 0956-053X. DOI: https://doi.org/10.1016/j.wasman.2017.10.026
Hassanzadeh, A., Zhang, F., Murphy, S.P., Pethybridge, S. J. and van Aardt, J. (2022) "Toward Crop Maturity Assessment via UAS-Based Imaging Spectroscopy—A Snap Bean Pod Size Classification Field Study", IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, pp. 1-17. ISSN 0196-2892. DOI: https://doi.org/10.1109/TGRS.2021.3134564
Huang, S., Tang, L., Hupy, J. P., Wang, Y. and Shao, G. (2021) "A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing", Journal of Forestry Research, Vol. 32, No. 1, pp. 1-6. ISSN 1007-662X. DOI: https://doi.org/10.1007/s11676-020-01155-1
Indolia, S., Goswami, A. K., Mishra, S. P. and Asopa, P. (2018) "Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach", Procedia Computer Science, Vol. 132, pp. 679-688. ISSN 1877-0509. DOI: https://doi.org/10.1016/j.procs.2018.05.069
Ishangulyyev, R., Kim, S. and Lee, S. (2019) "Understanding Food Loss and Waste—Why Are We Losing and Wasting Food?", Foods, Vol. 8, No. 8, p. 297. ISSN 2304-8158. DOI: https://doi.org/10.3390/foods8080297
Ji, Z., Pan, Y., Zhu, X., Wang, J. and Li, Q. (2021) "Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index", Sensors, Vol. 21, No. 4, p. 1406. ISSN 1424-8220. DOI: https://doi.org/10.3390/s21041406
Johnson, L. K., Dunning, R. D., Gunter, C. C., Bloom, J. D. Boyette, M. D. and Creamer, N. G. (2018) "Field measurement in vegetable crops indicates need for reevaluation of on-farm food loss estimates in North America", Agricultural Systems, Vol. 167, pp. 136-142. ISSN 0308-521X. DOI: https://doi.org/10.1016/j.agsy.2018.09.008
KC, K., Haque, I., Legwegoh, A. and Fraser, E. (2016) "Strategies to Reduce Food Loss in the Global South", Sustainability, Vol. 8, No. 7, p. 595. ISSN 2071-1050. DOI: https://doi.org/10.3390/su8070595
Khatri-Chhetri, A., Costa Jr. C. and Wollenberg, E. (2022) "Greenhouse gas mitigation co-benefits across the global agricultural development programs", Global Environmental Change, Vol. 76, p. 102586. ISSN 0959-3780. DOI: https://doi.org/10.1016/j.gloenvcha.2022.102586
Koester, U. and Galaktionova, E. (2021) "FAO Food Loss Index methodology and policy implications", Studies in Agricultural Economics, Vol. 123.1, pp. 1-7. ISSN 2063-0476. DOI: https://doi.org/10.7896/j.2093
Luo, N., Olsen, T., Liu, Y. and Zhang, A. (2022) "Reducing food loss and waste in supply chain operations", Transportation Research Part E: Logistics and Transportation Review, Vol. 162, p. 102730. ISSN 1366-5545. DOI: https://doi.org/10.1016/j.tre.2022.102730
Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J. M., McNairn, H. and Rao, Y. S. (2020) "Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data", Remote Sensing of Environment, Vol. 247, p. 111954. ISSN 0034-4257. DOI: https://doi.org/10.1016/j.rse.2020.111954
Marston, L. T., Read, Q. D., Brown, S. P. and Muth, M. K. (2021) "Reducing Water Scarcity by Reducing Food Loss and Waste", Frontiers in Sustainable Food Systems, Vol. 5. ISSN 2571-581X. DOI: https://doi.org/10.3389/fsufs.2021.651476
Moher, D. (2009) "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement", Annals of Internal Medicine, Vol. 151, No. 4, p. 264. ISSN 1743-9191. DOI: https://doi.org/10.1016/j.ijsu.2010.02.007
Otchere, D. A., Arbi Ganat, T. O., Gholami, R. and Ridha, S. (2021) "Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models", Journal of Petroleum Science and Engineering, Vol. 200, p. 108182. ISSN 0920-4105. DOI: https://doi.org/10.1016/j.petrol.2020.108182
Pagani, M., De Menna, F., Johnson, T. G. and Vittuari, M. (2020) "Impacts and costs of embodied and nutritional energy of food losses in the US food system: farming and processing (Part A)", Journal of Cleaner Production, Vol. 244, p. 118730. ISSN 0959-6526. DOI: https://doi.org/10.1016/j.jclepro.2019.118730
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., Luke A McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P. and Moher, D. (2021) "The PRISMA 2020 statement: an updated guideline for reporting systematic reviews", BMJ, Vol. 371, No. 71. ISSN 1756-1833. DOI: https://doi.org/10.1136/bmj.n71
Rayyan (2023) "Faster systematic reviews" [Online]. Available: https://www.rayyan.ai/[Accessed: Dec. 2, 2023].
Redlingshöfer, B., Coudurier, B. and Georget, M. (2017) "Quantifying food loss during primary production and processing in France", Journal of Cleaner Production, Vol. 164, pp. 703/714. ISSN 0959-6526. DOI: https://doi.org/10.1016/j.jclepro.2017.06.173
Rokni, K. and Musa, T. A. (2019) "Normalized difference vegetation change index: A technique for detecting vegetation changes using Landsat imagery", CATENA, Vol. 178, pp. 59-63. ISSN 0341-8162. DOI: https://doi.org/10.1016/j.catena.2019.03.007
Sharifi, A. (2020) "Remotely sensed vegetation indices for crop nutrition mapping", Journal of the Science of Food and Agriculture, Vol. 100, No. 14, pp. 5191-5196. ISSN 1097-0010. DOI: https://doi.org/10.1002/jsfa.10568
Shee, A., Parmar, A., Raut, S., Strum, B. and Bennett, B. (2022) "Assessing the measurement methods of post-harvest food loss and waste: opportunities and challenges", Enterprise Development & Microfinance, Vol. 33, No. 4. ISSN 1755-1986. DOI: https://doi.org/10.3362/1755-1986.22-00062
Sishodia, R. P., Ray, R. L. and Singh, S. K. (2020) "Applications of Remote Sensing in Precision Agriculture: A Review", Remote Sensing, Vol. 12, No. 19, p. 3136. ISSN 2072-4292. DOI: https://doi.org/10.3390/rs12193136
Soltani, A., Alimagham, S. M., Nehbandani, A., Torabi, B., Zeinali, E., Zand, E., Vadez, V., van Loon, M. P. and van Ittersum, M. K. (2020) "Future food self-sufficiency in Iran: A model-based analysis", Global Food Security, Vol. 24, p. 100351. ISSN 2211-9124. DOI: https://doi.org/10.1016/j.gfs.2020.100351
Spang, E. S., Moreno, L. C., Pace, S. A., Achmon, Y., Donis-Gonzalez, I., Gosliner, W. A., Jablonski-Sheffield, M. P., Momin, M. A., Quested, T. E., Winans, K. S. and Tomich, T. P. (2019) "Food Loss and Waste: Measurement, Drivers, and Solutions", Annual Review of Environment and Resources, Vol. 44, No. 1, pp. 117-156. ISSN 1543-5938. DOI: https://doi.org/10.1146/annurev-environ-101718-033228
Sun, H., Sun, Y., Jin, M., Ripp, S. A., Sayler, G. S. and Zhuang, J. (2022) "Domestic plant food loss and waste in the United States: Environmental footprints and mitigation strategies", Waste Management, Vol. 150, pp. 202-207. ISSN 0956-053X. DOI: https://doi.org/10.1016/j.wasman.2022.07.006
Swartz, M. K. (2011) "The PRISMA Statement: A Guideline for Systematic Reviews and Meta-Analyses", Journal of Pediatric Health Care, Vol. 25, No. 1, pp. 1-2. ISSN 0891-5245. DOI: https://doi.org/10.1016/j.pedhc.2010.09.006
Wang, H., Li, T., Nishida, E., Kato, Y., Fukano, Y. and Guo, W. (2023) "Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income", Plant Phenomics, Vol. 5. ISSN 2643-6515. DOI: https://doi.org/10.34133/plantphenomics.0086
Xie, G., Ma, W., Peng, H., Li, R. and Li, K. (2021) "Price Performance-Driven Hardware Cost Optimization Under Functional Safety Requirement in Large-Scale Heterogeneous Distributed Embedded Systems", IEEE Transactions on Industrial Electronics, Vol. 68, No. 5, pp. 4485-4497. ISSN 1557-9948. DOI: https://doi.org/10.1109/TIE.2019.2905815
Xue, L., Liu, G., Parfitt, J., Liu, X., Van Herpen, E., Stenmarck, Å., O’Connor, C., Östergren, K. and Cheng, S. (2017) "Missing Food, Missing Data? A Critical Review of Global Food Losses and Food Waste Data", Environmental Science & Technology, Vol. 51, No. 12, pp. 6618-6633. ISSN 0013-936X. DOI: https://doi.org/10.1021/acs.est.7b00401
Yu, J. and Muñoz-Justicia, J. (2020) "A Bibliometric Overview of Twitter-Related Studies Indexed in Web of Science", Future Internet, Vol. 12, No. 5, p. 91. ISSN 1999-590. DOI: https://doi.org/10.3390/fi12050091
Zhu, J., Luo, Z., Sun, T., Li, W., Zhou, W., Wang, X., Fei, X., Tong, H. and Yin, K. (2023) "Cradle-to-grave emissions from food loss and waste represent half of total greenhouse gas emissions from food systems", Nature Food, Vol. 4, No. 3, pp. 247-256. ISSN 2662-1355. DOI: https://doi.org/10.1038/s43016-023-00710-3
