Smart irrigation system and early plant disease detection using IoT and novel non-linear growing self-organizing map based artificial neural network

Abstract

Abstract The safety of the global food supply depends heavily on effective crop management, making early diagnosis of plant diseases vital for improving agricultural productivity. This proposal outlines the development of an intelligent irrigation system that utilizes machine learning and the Internet of Things (IoT) for the early detection of sugarcane leaf diseases and assessment of their impact on crop yield. The system gathers and analyzes data on soil temperature, humidity, and leaf characteristics—specifically changes in texture and color—using high-resolution photography from unmanned aerial vehicles (UAVs) and IoT-connected sensors. To enhance feature extraction and classification, the system employs a non-linear growing self-organizing map (NG-SOM) embedded within the hidden layers of an artificial neural network (ANN). This advanced model effectively identifies complex patterns in the collected data. Compared to traditional classification methods, this approach achieves a sugarcane disease detection accuracy of 95.6% and reduces false positives by 18.3%. It has been tested on multiple disease types, including red rot, smut, and rust. Additionally, the integration of early diagnosis with intelligent irrigation shows a strong correlation with optimized crop production. Predictive modeling of disease progression based on early detection improves output projections by 22.4%, demonstrating the system’s value in precision agriculture. By merging UAV imaging, sensor-based monitoring, and advanced machine learning, this approach offers a promising solution for proactive crop disease management and sustainable yield enhancement in sugarcane farming.

Plant phenotyping relevance

UAV画像、IoTセンサー、機械学習を統合し、サトウキビ葉の色・テクスチャから病害状態を検出する方法の開発と評価が中心である。

Code and data availability

The paper's own sugarcane UAV imagery, IoT sensor data, and NG-SOM-ANN analysis are not publicly deposited; the Data Availability Statement states they are held by the corresponding author and shared only on email request. The two dataset URLs appearing in the reference list (Kaggle rice leaf diseases, RiceSeedling UAV

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