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

## TL;DR

This paper introduces an IoT-based smart irrigation system and a novel neural network for early detection of sugarcane leaf diseases, improving crop yield and reducing false positives.

## Contribution

A novel non-linear growing self-organizing map-based artificial neural network for plant disease detection and irrigation optimization.

## Key findings

- The system achieves 95.6% accuracy in sugarcane disease detection.
- False positives are reduced by 18.3% compared to traditional methods.
- Early diagnosis combined with irrigation improves output projections by 22.4%.

## 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.