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

I-Corps项目更广泛的影响/商业潜力是开发软件技术，为农民提供基于农田实时传感数据分析的数据驱动建议。拟议的技术还将引入智能连接农场（SCF）的概念，使农民和服务提供商能够通过无线技术共享保护隐私的数据。目标是在不同的农场之间传播实时信息，并做出精确的决策，比如分析昆虫数量的增长。在农村农场实施SCF网络存在许多网络和技术挑战，包括传输数据的大小和异构性、低/无带宽通信以及数据隐私。该技术采用低成本的通信介质和边缘设备，为农民提供更好的成本效益权衡。此外，通过保护隐私的分布式方法解决数据隐私问题，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位置泄露有关作物质量的信息。提出的技术还包括农学家和昆虫学家等领域专家提供精确的建议。I-Corps项目的基础是开发一个与保护隐私的分布式数据分析相结合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，该平台提供了基于位置的细粒度处方，为新手农民精确施用杀虫剂，降低了农业成本。该处方的目的是提供有关精确感染区域、感染严重程度和喷洒杀虫剂数量的信息。此外，它还可以用来建立一个农场网络，使用远程（LoRa）通信技术跨场，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。提出的技术可以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，实时推荐杀虫剂喷洒决策。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.