Smartphone Application Development for Rice Field Management Through Aerial Imagery and Normalised Difference Vegetation Index (NDVI) Analysis

Abstract

In the current practices, farmers typically rely on the traditional method paper-based for farming data records, which leads to human error. However, the paper-based system can be improved by the mobile app technology to ease the farmers acquiring farm data as all of the farm information will be stored in digital form. This study aimed to develop a smartphone agricultural management app known as Padi2U and implement User Acceptance Test (UAT) for end-users. Padi2U was developed using Master App Builder software and integration with the multispectral imagery. Padi2U provides recommendations based on the Department of Agriculture’s (DOA), such as rice check, pest and disease control, and weed management. Through the Padi2U, farmers can access the field data to understand the crop health status online using the Normalised Difference Vegetation Index (NDVI) map derived from the multispectral images. The NDVI is correlated to the Soil Plant Analysis Development (SPAD) value, corresponding to R² = 0.4012. UAT results showed a 100 percent satisfaction score with suggestions were given to enhance the Padi2U performance. It shows that Padi2U can be improved to help farmers in the field monitoring virtually by integrating multispectral imagery and information from the field.

Mobile Computing for Pest and Disease Management Using Spectral Signature Analysis: A Review

“…Those spectral signatures are conveniently accessible via mobile devices that have installed the online datasets. The mobile applications have a database of the problems encountered, including a spectral signature graph, which differentiates it from other existing mobile applications such as WeedID and Padi2U that simply provide an image of the pest, a detailed description, and the control method [102,103]. In the future, mobile applications could be used as a reference for the user to view the spectral signature for each pest and disease.…”

Weed Detection in Rice Fields Using UAV and Multispectral Aerial Imagery

“…Unmanned aerial vehicle (UAV) remote sensing systems have become a popular topic worldwide because they are mobile, rapid, and economic [8]. Moreover, it has potential as an alternative given its low cost of operation in environmental monitoring and agriculture application.…”

“…UAVs can produce aerial images implanted with different data depending on the sensors utilized, such as multispectral camera, RGB camera, hyperspectral camera, and thermal sensor. UAVs have the capacity to cover large zones in a brief space of time and the payload capacity to carry optical sensors [8]. The images from UAVs and sensors later undergo processing to form an important feature that is understandable for the end-users.…”

Real-Time IoT-Enabled Water Management for Rooftop Urban Agriculture Using Commercial Off-the-Shelf Products

“…Several studies have investigated the potential of IoT for crop production, including works that used temperature and soil moisture as indicating parameters [4,5]. When used in agricultural applications, some researchers found evidence that IoT helps in soil management and nutrient detection [6].…”