农场护理:基于位置的作物推荐系统与疾病识别

W. Weerasooriya, Anudi Disara Wanigaratne, Hashini De Silva, S.A.Hiran Hansaka, J. Perera, Laneesha Rukgahakotuwa
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引用次数: 1

摘要

斯里兰卡自古以来就是一个农业国家。今天的农业正处于危险的境地,因为农民正在失去他们的产量。种植作物时需要考虑很多因素,如降雨量、温度、土壤条件、未来价格、疾病等。我们决定通过我们正在制作的android应用程序来帮助他们。在这里,我们确定了四个主要问题。首先,是错误的作物种植。这是农作物和耕作遭到破坏的主要原因。为了解决这个问题,我们根据它们的地理位置提出了五种最适合种植的作物。第二个问题是缺乏对未来市场价格的了解。为了解决这个问题,我们预测了未来12个月每种煤的价格。另一个问题是他们无法以合理的价格出售产品。在这里,我们直接连接买家和卖家,不需要中介。最后一个问题是难以识别受作物影响的疾病。使用我们的移动应用程序,农民可以通过将图像上传到应用程序来识别哪些疾病影响了他们的作物。为了解决上述问题,我们使用了随机森林、k-means聚类和卷积神经网络算法等机器学习算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FarmCare: Location-based Profitable Crop Recommendation System with Disease Identification
Sri Lanka is an agricultural country since ancient times. Today’s agriculture field is in a dangerous situation because farmers are losing their yield. There are many factors to consider when planting crops like rainfall, temperature, soil conditions, future prices, diseases, etc. We decided to help them through the android application we are making. Here we identified four main problems. First, it was wrong crop cultivation. This is the main reason crops and cultivation are destroyed. To give a solution to that problem, we suggest the five most suitable crops to cultivate according to their location. The second problem is a lack of knowledge about future market prices. As a solution to that problem, we predict prices for each cop for the next 12 months. Another problem is an inability to sell their product at a reasonable price. Here, we directly connect buyers and sellers by removing intermediaries. The last problem is the difficulty to identify diseases affected by crops. Using our mobile app farmers can identify which disease affected their crops by uploading an image to the app. To give solutions to the above-mentioned problems Machine Learning algorithms are used like Random Forest, k-means clustering, and Convolution Neural Network algorithms.
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