论文标题
农民助理：基于机器学习的农业解决方案的应用
Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions
论文作者
论文摘要
在种植不确定的灌溉，土壤质量等不确定的农作物时，农民面临几个挑战。尤其是在印度，大部分农民没有选择合适的农作物和肥料的知识。此外，由于疾病引起的农作物衰竭会对农民和消费者造成重大损失。尽管使用机器学习技术对这些疾病的自动检测有了最近的发展，但尚未充分探索对深度学习的利用。此外，由于训练中使用的高质量数据，缺乏计算能力以及模型的可推广性不佳，因此此类模型不容易使用。为此，我们创建了一个易于使用的开源Web应用程序，以解决其中一些问题，这可能有助于改善作物生产。特别是，我们支持作物建议，肥料推荐，植物疾病预测和交互式新闻。此外，我们还使用可解释性技术来解释我们的疾病检测模型的预测。
Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers do not have the knowledge to select appropriate crops and fertilizers. Moreover, crop failure due to disease causes a significant loss to the farmers, as well as the consumers. While there have been recent developments in the automated detection of these diseases using Machine Learning techniques, the utilization of Deep Learning has not been fully explored. Additionally, such models are not easy to use because of the high-quality data used in their training, lack of computational power, and poor generalizability of the models. To this end, we create an open-source easy-to-use web application to address some of these issues which may help improve crop production. In particular, we support crop recommendation, fertilizer recommendation, plant disease prediction, and an interactive news-feed. In addition, we also use interpretability techniques in an attempt to explain the prediction made by our disease detection model.
