Proposed Fuzzy-Stranded-Neural Network Model That Utilizes IoT Plant-Level Sensory Monitoring and Distributed Services for the Early Detection of Downy Mildew in Viticulture

Abstract

Novel monitoring architecture approaches are required to detect viticulture diseases early. Existing micro-climate decision support systems can only cope with late detection from empirical and semi-empirical models that provide less accurate results. Such models cannot alleviate precision viticulture planning and pesticide control actions, providing early reconnaissances that may trigger interventions. This paper presents a new plant-level monitoring architecture called thingsAI. The proposed system utilizes low-cost, autonomous, easy-to-install IoT sensors for vine-level monitoring, utilizing the low-power LoRaWAN protocol for sensory measurement acquisition. Facilitated by a distributed cloud architecture and open-source user interfaces, it provides state-of-the-art deep learning inference services and decision support interfaces. This paper also presents a new deep learning detection algorithm based on supervised fuzzy annotation processes, targeting downy mildew disease detection and, therefore, planning early interventions. The authors tested their proposed system and deep learning model on the grape variety of protected designation of origin called debina, cultivated in Zitsa, Greece. From their experimental results, the authors show that their proposed model can detect vine locations and timely breakpoints of mildew occurrences, which farmers can use as input for targeted intervention efforts.

A Cloud-Based Deep Learning Framework for Downy Mildew Detection in Viticulture Using Real-Time Image Acquisition from Embedded Devices and Drones

“…The authors propose a framework that uses vine-field RGB cameras as data inputs and tries to detect downy mildew or other vine diseases or viticulture stress caused by extreme environmental conditions at the vine level, similar to their thingsAI implementation [47], by utilizing RGB image data and deep learning object detection models. Their proposition was initially presented at [13] and modified accordingly to support periodic image and real-time video stream inferences.…”

Smart IoT Irrigation System Based on Fuzzy Logic, LoRa, and Cloud Integration

“…Agriculture 4.0 benefits from using several technologies already mature in other domains, such as the Internet of Things (IoT), big data, or artificial intelligence (AI), to optimize the agricultural processes [6]. For example, in [7], the authors' novel monitoring architecture approaches were required to detect viticulture diseases early, combining IoT and cloud computing. Among other issues, those technologies can help reduce water usage and increase its efficiency.…”

“…For example, the authors of [22] proposed a WiFi-based, low-cost system for environmental temperature, soil moisture, relative humidity, lighting monitoring, and remote on/off control. In [7], a fuzzy-stranded-neural network model detected downy mildew in viticulture early and successfully. In [23], the FL technique was used to monitor and set the duration for which the water pump would be turned on.…”