SmartPredi – Development of Agricultural Crop Wastage Reduction System using Machine Learning

W. Weerasinghe, K.W.A.M. Somawansha, K.A. Jayanga Chandrasiri, T.M.S.Y.B. Thalagahagedara, K.B.A Bhagyanie Chathurika, N.H.P. Ravi Supunya Swarnakantha
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Abstract

The culture and economy of Sri Lanka heavily depend on agriculture. The All-Island Farmers Federation (AIFF) claims that post-harvest produce loss is a problem that has plagued farmers in all regions of Sri Lanka and occurs both on farms and in commercial locations. The lack of a suitable system to handle produce, such as fruits and vegetables, has been identified as the key problem. The process of sowing seeds to generating the harvest and transporting it to the consumers is an overly complex process. If this process is not correctly identified the demand and supply may not be at equilibrium. Farmers tend to take decisions based on their experiences or from the knowledge gathered from past generations. Over the year environmental factors as well as economic factors have changed, therefore there is a high chance that the decisions taken by farmers might lead to wastage of crops. This research hopes to produce a mobile application for the farmers by considering some factors that affect the wastage in crops and try to provide timely relevant information to minimize the crop wastage by deploying machine learning, one of the advanced technologies in crop prediction.
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利用机器学习技术开发农业作物减量系统
斯里兰卡的文化和经济严重依赖农业。全岛农民联合会(AIFF)声称,收获后的农产品损失是困扰斯里兰卡所有地区农民的一个问题,既发生在农场,也发生在商业场所。缺乏一个合适的系统来处理农产品,如水果和蔬菜,已被确定为关键问题。从播种到收获再到将其运送到消费者手中是一个过于复杂的过程。如果这个过程没有被正确地识别,需求和供给可能就不会处于平衡状态。农民倾向于根据他们的经验或从过去几代人那里收集的知识做出决定。在过去的一年中,环境因素和经济因素都发生了变化,因此农民做出的决定很有可能导致作物浪费。本研究希望通过考虑影响作物浪费的一些因素,为农民制作一个移动应用程序,并尝试通过利用作物预测中的先进技术之一机器学习,及时提供相关信息,以最大限度地减少作物浪费。
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