Agrotechnology

Utilizing Remote Sensing for Crop Pest and Disease Detection and Management

Ewert Ryo^* Department of Agricultural Landscape, Brandenburg University of Technology, Cottbus, Germany
^*Correspondence: Ewert Ryo, Department of Agricultural Landscape, Brandenburg University of Technology, Cottbus, Germany, Email:

Received: 04-Aug-2023, Manuscript No. AGT-23-23029; Editor assigned: 07-Aug-2023, Pre QC No. AGT-23-23029 (PQ); Reviewed: 21-Aug-2023, QC No. AGT-23-23029; Revised: 28-Aug-2023, Manuscript No. AGT-23-23029 (R); Published: 04-Sep-2023, DOI: 10.35248/2168-9881.23.12.332

Description

Remote sensing is the technique of acquiring information about an object or a phenomenon without making physical contact with it. Remote sensing can be used for various applications in agriculture, such as crop monitoring, mapping, and management. One of the important applications of remote sensing is the detection and control of crop pests and diseases, which are major threats to food security and agricultural sustainability.

Crop pests and diseases can cause significant losses in crop yield and quality, as well as increase the cost of production and environmental impact. Therefore, timely and accurate detection and control of crop pests and diseases are essential for effective pest management and crop protection. However, traditional methods of pest and disease detection, such as field surveys, visual inspection, and laboratory analysis, are often laborintensive, time-consuming, costly, and subjective. Moreover, these methods may not be able to capture the spatial and temporal variability of pest and disease occurrence and severity, which can affect the decision making and implementation of pest control measures.

Remote sensing can overcome some of the limitations of traditional methods by providing large-scale, high-resolution, and timely information on crop health status and pest and disease infestation. Remote sensing can also provide information on environmental factors that influence pest and disease development, such as soil moisture, temperature, humidity, etc. Remote sensing can be performed using various platforms and sensors, such as satellites, aircrafts, Unmanned Aerial Vehicles (UAVs), or ground-based devices. These platforms and sensors can capture different types of data, such as optical, thermal, radar, or hyper spectral images.

Remote sensing data can be analyzed using various techniques, such as image processing, machine learning, or artificial intelligence. These techniques can help to extract useful information from the data, such as spectral indices, vegetation indices, biophysical parameters, or classification maps. This information can then be used to identify and quantify the presence and severity of crop pests and diseases, as well as to assess their impact on crop yield and quality. Remote sensing data can also be integrated with other sources of information, such as Geographic Information Systems (GIS), crop models, or weather data, to enhance the accuracy and reliability of pest and disease detection and control.

There are many examples of how remote sensing has been used for pest and disease detection and control in crops. For instance, presents a method for detecting cotton root rot using airborne imagery integrated with variable rate technology. Cotton root rot is a soil borne fungal disease that causes severe losses in cotton production. The authors use airborne multispectral imagery to map the spatial distribution of cotton root rot in a field. They then use this map to generate a prescription map for site-specific fungicide application using variable rate technology. They also evaluate the performance of this method by comparing it with uniform fungicide application.

In conclusion, remote sensing is a valuable tool for pest and disease detection and control in crops, as it can provide accurate, timely, and scalable information that can support pest management and crop protection. However, there are also challenges and limitations that need to be addressed, such as data availability and quality, computational complexity and cost, algorithm selection and evaluation, etc.