Remote sensing is a tool to gather the information about an object or any phenomenon without direct contact or damaging the objects. This technology had numerous application and one of it is in agriculture. Unlike tradition agriculture practiced that difficult to execute and required a large number of man power, implementing this technology will increase the production yield of the crops and improved the agriculture sector in managing and controlling. Remote sensing were able to forecast the crop production, identified the crop type, assess the crop damage and monitoring its progress. Therefore, this research was conducted in order to monitor the early stage of growth of rice crop planted by the farmers in the paddy field using remote sensing. To do so, popular empirical vegetation index known as Normalized Difference Vegetation Index (NDVI) generated from unmanned aerial vehicle (UAV) was selected to monitor the changes of rice crop starting from the day it been planted until eleventh day of planted. Early stage of monitoring the crop growth using NDVI is a best approach to practice. Any damages that occur during this stage will affect the yield production and economy. Result from image analysis shown that NDVI were able to observe the rice crop growth and able to locate the damage part in the paddy plot. Fast action can be made by the farmers to counter attack the damage and treat the problematic points.
The PadiU Putra rice line is a blast-resistant and high-yield rice line with high potential. The application of topdressing and the foliar applied method of silicon (Si) treatments could strengthen the culm to resist breakage and ultimately increase yield production. Treatments which consisted of a control, a Si topdressing, and a Si foliar applied were arranged in a randomised complete block design. At 55 days after transplanting (DAT), the foliar applied Si treatments had 59% higher dry matter partitioning to the roots. Meanwhile, at 75 DAT, both Si foliar applied and topdressing method showed increased assimilate partitioning into the culm sheath by 29% and 49%, respectively. Dark green and light yellowish colours were obtained in both Si treatments using UAV, indicating similar results to physiological responses. Remarkably, Si foliar applied treatments enhanced the diameter and width of the outer and inner layers of the diameter of vascular bundles at 75 DAT by 58, 181, and 80%, respectively. The yield production of rice increased by 53% in the Si foliar applied, compared to the control, and produced a 1.63 benefit-cost ratio.
This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.
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.
Rice is the staple food for most people in Southeast Asia, mainly Malaysia. Unfortunately, Malaysia does not reach a 100% self-sufficiency level on rice production due to inefficiency of rice farm management, pest and disease outbreak, poorly irrigation system, and climate change. Each spectral band of electromagnetic signature in the rice crops can be identified to analyse the crop condition based on the reflectance value. Therefore, unmanned aerial vehicle (UAV) can capture different spectral band images of the rice field depending on the sensors used. This study aims to produce a paddy growth map based on the normalized difference vegetative index (NDVI) value and validate the paddy growth map using the soil plant analysis development (SPAD) data. This study was carried out at the paddy field planted with PadiU Putra rice variety in Muda Agricultural Development Authority (MADA), Jitra in Kedah. Three reading samples for each point at the paddy field within 1 m radius were recorded. Then, the samples from each point were scanned using SPAD chlorophyll meter. The image data were collected using multispectral and RGB cameras at the altitude of 60 m, and a calibrated reflectance panel was used to calibrate the image. Ground control point (GCP) was placed at the four corners of the study plot, and it was being used as a georeferencing point for aerial imagery mapping. Those images were undergone orthomosaic process to produce a single overlapped image. NDVI was used to measure the healthy level of rice crops. NDVI map had shown the distribution of NDVI value across the study plot, which includes the healthy and less healthy vegetative area. SPAD value has no significant relationship with the aerial imagery of NDVI value. The NDVI map allows the farmers to monitor the paddy growth status and effectively improve their rice farm management. In the future, advanced classification methods based on the reflectance of weed, water, and soil can be prioritized and separated into different classes, whereby the NDVI map can be plotted on the paddy crops.
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