# Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning

arXiv: https://arxiv.org/abs/2107.04895 · HTML: https://mlsanity.com/paper/2107.04895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 10 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)

## Built on by (most cited first, 1 of 1)

- [Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges](https://mlsanity.com/paper/2308.06668/) (2023)

## Abstract (arXiv)

Precision agriculture system is an arising idea that refers to overseeing farms utilizing current information and communication technologies to improve the quantity and quality of yields while advancing the human work required. The automation requires the assortment of information given by the sensors such as soil, water, light, humidity, temperature for additional information to furnish the operator with exact data to acquire excellent yield to farmers. In this work, a study is proposed that incorporates all common state-of-the-art approaches for precision agriculture use. Technologies like the Internet of Things (IoT) for data collection, machine Learning for crop damage prediction, and deep learning for crop disease detection is used. The data collection using IoT is responsible for the measure of moisture levels for smart irrigation, n, p, k estimations of fertilizers for best yield development. For crop damage prediction, various algorithms like Random Forest (RF), Light gradient boosting machine (LGBM), XGBoost (XGB), Decision Tree (DT) and K Nearest Neighbor (KNN) are used. Subsequently, Pre-Trained Convolutional Neural Network (CNN) models such as VGG16, Resnet50, and DenseNet121 are also trained to check if the crop was tainted with some illness or not.