Design and development of dynamic Agri-ontology for IoT interoperability

In recent years, the usage and applications of Internet of Things (IoT) have increased exponentially. IoT connects multiple heterogeneous devices like sensors, micro controllers, actuators, smart devices like mobiles, watches, etc. IoT contributes the data produced in the context of data collection, including the domains like military, agriculture, healthcare, etc. The diversity of possible applications at the intersection of the IoT and the web semantics has prompted many research teams to work at the interface between these two disciplines. This makes it possible to collect data and control various objects in transparent way. The challenge lies in the use of this data. Ontologies address this challenge to meet specific data needs in the IoT field. This paper presents the implementation of a dynamic agriculture ontology-building tool that parses the ontology files to extract full data and update it based on the user needs. The technology is used to create the angular library for parsing the OWL files. The proposed ontology framework would accept user-defined ontologies and provide an interface for an online updating of the owl files to ensure the interoperability in the agriculture IoT.     

Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative, and prominent features. Moreover, the proposed model is trained over the PlantVillage (PV), Data Repository of Leaf Images (DRLI), and a new Plant Composite (PC) dataset individually, and later on test samples, its actual performance is evaluated. After extensive experimental analysis, the proposed model obtained 1.00%, 0.96% and 0.99% accuracies on all three included datasets. Moreover, the proposed method achieves better inference speed when compared with other State-Of-The-Art (SOTA) approaches. In addition, a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pre-trained models and other Machine Learning (ML) and DL methods.     

Development,integration, and field evaluation of an autonomous citrus-harvesting robot

Citrus harvesting is a labor-intensive and time-intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus-harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus-harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus-harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high-precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end-effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus-harvesting robots.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Closed-loop drip irrigation control using a hybrid wireless sensor and actuator network

In this research, a closed-loop drip irrigation control hybrid wireless sensor and actuator network (HWSAN) prototype were developed and deployed in a crop field for soil property precise measurement and precision irrigation in accordance with the measured soil property. The HWSAN was composed of a wireless sensor and actuator network (WSAN) used for in-field soil property monitoring and irrigation control and a laboratory supervising system. The WSAN included ten sensor nodes, five irrigation control nodes...     

Optimization of quality and operational costs through improved scheduling of harvest operations

The agriculture sector still lacks the tools and models to enhance the utilization of different resources. This paper addresses the vineyard harvesting problem in developing countries, with the objective of optimizing the wine quality and minimizing the operational costs. Heuristics were introduced to better assign the harvesting days to the different grape blocks that exist in the vineyard's field. The quality of the grapes was a key target as it can transform production from a pinnacle wine to a bulk one. We solved several numerical examples for verification and demonstrative purposes and found that our proposed approach finds solutions that significantly reduce the harvesting costs in the vineyard and considerably outperform Branch and Bound algorithm especially for large problems.     

移动终端的设施农业物联网环境监控系统设计

通过使用物联网(IOT)传输协议—消息队列遥测传输(MQTT)协议,将前端硬件、服务器端及移动端连接在一起,完成对设施农业环境的远程实时监控.在系统中,前端硬件以STM32微处理器为核心,由传感器模块、设备控制模块、GPRS模块和电源模块组成,实现数据的采集和传输;服务器端有数据接收、存储、分析的能力,并担任MQTT消息协议中消息生产者和消息管理者的角色;移动终端软件则以Android操作系统为基础开发了应用软件,用户在装有Android系统的移动终端上安装该应用软件,即可查看实时环境情况,发送控制设备指令等.经过测试,整个系统运行稳定、使用简单、实时性高,用户能有效地对设施农业环境进行远程实时监控.     

云计算农田会商平台规划和设计

面向西北旱区农业,基于云计算、大数据和物联网技术的集成应用会商平台,利用卫星、无人机和地面传感网等多遥感平台,对耕地平整、水肥药精准作业装备进行作业调度,形成精准作业调度、遥感的空间决策支持、多源农情信息视频会商、以及农情遥感信息适时发布系统于一体的综合性、业务化的会商服务平台.该平台提供专家会商、水肥管理、病害诊断防治、旱情预测预报和农业遥感信息适时发布等服务.     

基于ETLB-DEEC的精细农业无线传感器网络研究

无线传感器网络技术应用于精细农业信息获取与管理已成为一种趋势和必然,针对WSN在大规模农田种植监测领域中所面临的监测面积大、监测时间长、低功耗要求、地势复杂以及作物多样等问题,深入研究基于DEEC的等边三角形节点部署的负载均衡成簇协议ETLB-DEEC(Equilateral Triangle Load Balancing-DEEC),采用等边三角形网格节点部署方式与网络分区,提出"孤儿节点"和"收容节点"的概念.在此基础上,设计一种面向基站最短路径簇间多跳寻优机制,可找寻多条最短路径协议.仿真结果表明,改进的协议具备高效节省传感器节点数、实现全局网络负载均衡,提高网络能量利用率,延长WSN整体生命周期等功能和特点.     

智慧农业控制系统的服务软件设计

基于Android技术、计算机网络技术设计了智慧农业控制系统手机APP客户端、上位机软件,搭建了服务器.上位机作为服务器和硬件控制系统中介,实时接收硬件系统的农作物环境信息,并通过服务器保存.APP客户端实时获取服务器信息,直观形象呈现给用户,且可以设置参数报警阀值,及时提醒用户,用户可选择智能模式或者手动模式远程调节农作物生长环境因素.