I-Corps: Developing a Robust and Automated Insect Detection System for Connecting Farm Communities

这个i-Corps项目的更广泛的影响/商业潜力是开发软件技术，根据来自他们农场的实时感官数据分析为农民提供数据驱动的建议。拟议的技术还将引入智能互联农场(SCF)的概念，使农民和服务提供商能够通过无线技术共享隐私保护的数据。其目标是在不同的农场传播实时信息，并做出精确的决策，如分析昆虫种群增长。在农村农场实施SCF网络面临许多网络和技术挑战，包括传输数据的大小和异构性、低带宽/无带宽通信以及数据隐私。建议的技术采用低成本的通信媒介和边缘设备，为农民提供更好的成本效益权衡。此外，数据隐私通过保护隐私的分布式方法来解决，该方法提供安全和个性化的数据收集和共享，以消除通过GPS定位泄露有关作物质量的信息。这项拟议的技术还包括农学家和昆虫学家等领域的专家提供准确的建议。这个i-Corps项目基于开发一个集成了隐私保护的分布式数据分析的农村连接和社区决策平台。收集的数据用于训练机器学习模型。与现有技术相比，该平台为新手农民精确使用杀虫剂提供了基于位置的细粒度处方，降低了农业成本。该处方旨在提供有关准确的感染区域、感染的严重程度和喷洒的杀虫剂数量的见解。此外，它还可用于在整个田地建立使用远程(LORA)通信技术的农场网络，使其适用于没有网络的地区将现场数据传输到边缘设备。拟议的技术解决了用户的适应性、信任、数据安全、隐私和风险偏好等问题，以证明农民在收集和处理实时农场数据方面的好处是合理的。拟议的技术可应用于开发广泛的网络物理系统和智能服务。这样的系统可能有利于作物顾问、农学家、数据科学家和昆虫学家收集农场数据，并完成各种应用，如昆虫检测，以实时建议杀虫剂喷洒决定。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of software technology to provide farmers with data-driven recommendations based on real-time sensory data analysis from their farm fields. The proposed technology also will introduce the concept of Smart Connected Farms (SCF) to enable privacy-preserving data sharing among farmers and service providers via wireless technology. The goal is to disseminate real-time information across different farms and make precise decisions like analyzing insect population growth. Implementing an SCF network in rural farms has numerous networking and technological challenges, including the size and heterogeneity of transmitted data, low/no bandwidth communications, and data privacy. The proposed technology employs low-cost communication medium and edge devices to provide better cost-benefit tradeoffs to the farmers. In addition, data privacy is addressed with a privacy-preserving distributed approach that offers secure and personalized data collection and sharing to eliminate revealing information about the crop quality with a GPS location. The proposed technology also involves the domain experts like agronomists and entomologists to provide precise recommendations.This I-Corps project is based on the development of a platform for rural connectivity and community decision-making integrated with privacy-preserving distributed data analytics. The data collected is used to train machine learning models. Compared to existing technologies, the proposed platform offers a location-based fine-grained prescription for the precise application of insecticides for a novice farmer, reducing farming cost. The prescription is designed to provide insights about a precise infected area, the severity of the infestation, and the number of insecticides to be sprayed. In addition, it may be used to establish a farm network using Long Range (LoRa) communication technology across the field, making it suitable for areas with no network to transmit on-field data to edge devices. The proposed technology addresses the user's issues of adaptability, trust, data security, privacy, and risk preferences to justify the benefits to farmers in collecting and processing real-time farm data. The proposed technology may be applied to developing a broad range of cyber-physical systems and smart services. Such systems may benefit crop consultants, agronomists, data scientists, and entomologists to collect on-farm data and accomplish various applications such as insect detection to recommend insecticide spraying decisions in real time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.