The problem faced by one farmer can also be the problem of some other farmer in other regions. Providing information to farmers and connecting them has always been a challenge. Crowdsourcing and community building are considered as useful solutions to these challenges. However, privacy concerns and inactivity of users can make these models inefficient. To tackle these challenges, we present a cost-efficient and blockchain-based secure framework for building a community of farmers and crowdsourcing the data generated by them to help the farmers’ community. Apart from ensuring privacy and security of data, a revenue model is also incorporated to provide incentives to farmers. These incentives would act as a motivating factor for the farmers to willingly participate in the process. Through integration of a deep neural network-based model to our proposed framework, prediction of any abnormalities present within the crops and their predicted possible solutions would be much more coherent. The simulation results demonstrate that the prediction of plant pathology model is highly accurate.

一个农民面临的问题也可能是其他地区其他农民的问题。向农民提供信息并与他们建立联系一直是一个挑战。众包和社区建设被认为是应对这些挑战的有用解决方案。但是，隐私问题和用户的不活动可能会使这些模型效率低下。为了应对这些挑战，我们提出了一种基于成本效益的，基于区块链的安全框架，用于建立农民社区并将他们产生的数据众包以帮助农民社区。除了确保数据的私密性和安全性外，还采用了收入模型来激励农民。这些激励措施将成为农民自愿参与这一过程的激励因素。通过将基于深度神经网络的模型集成到我们提出的框架中，对农作物内存在的任何异常及其可能的解决方案的预测将更加连贯。仿真结果表明，植物病理模型的预测是高度准确的。