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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...
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... Given the advancement in agricultural production, the significance of technology and data-driven decision-making in farming is crucial (Gunavathie et al., 2023;Pongnumkul et al., 2015;Roslin et al., 2021). The mobile application designed for rice farmers in the Sanamchai Khet region was an example of this evolving agricultural innovation. ...
... The advent of this application could mark a significant shift from traditional, experience-based decision-making to a more data-driven approach. When real-world, current data inform decisions about planting, harvesting, and selling, it increases the likelihood of positive outcomes, reduces risks, and can lead to more consistent yields and profits (Llones and Suwanmaneepong, 2021a;Roslin et al., 2021). ...
... This is particularly evident from the high satisfaction ratings about the application's features and contents, which suggested that farmers value accurate, reliable, and pertinent information. In an era where precision agriculture becomes vital, such platforms bridge the information gap, facilitating optimized, data-backed farming methods (Roslin et al., 2021). In addition, with accessible data on costs and returns, farmers might move towards standardized practices that have proven effective, reducing the trial-and-error approach (Pongnumkul et al., 2015). ...
Using descriptive analysis, the results revealed that the rice production database, accessible through the mobile application, incorporates critical variables encompassing plantation areas, variable and fixed costs, total costs, yields, selling prices, income, profitability or loss, and breakeven points. The assessment of acceptance across three dimensions included content, technical and physical attributes, and perceived ease of use, that the farmers consistently demonstrated the highest level of acceptance for the mobile application. In terms of satisfaction assessment, encompassing design, content, performance, and utility, the findings uniformly indicated that farmers expressed substantial satisfaction with the rice production database accessible via the mobile application. Farmers' feedback underscored the application's user-friendliness and its potential for aiding production planning in subsequent seasons. Moreover, the facilitating the adoption of innovative platforms, and policymakers can empower farmers with tools to enhance productivity and induce satisfaction and confidence in modern agricultural practices.
... By connecting a network, farmers could retrieve real-time data via smartphones to help farmers with contingency plans or crop maintenance (Ali et al., 2020). Locally, Padi2U mobile app was developed by Roslin et al. (2021) to monitor paddy health status in a paddy plot study at Ladang Merdeka, Ketereh, Kelantan. Additionally, the authors highlighted that the future crop monitoring status using IoT and mobile apps is one of the effective ways for farmers to monitor and manage their crops. ...
The study and literature on the Internet of Things (IoT) and its applications in agriculture for smart farming are increasing worldwide. However, the knowledge mapping trends related to IoT applications in plant disease, pest management, and control are still unclear and rarely reported. The primary aim of the present study is to identify the current trends and explore hot topics of IoT in plant disease and insect pest research for future research direction. Peer review articles published from Web of Science (WoS) Core Collection (2010-2021) were identified using keywords, and extracted database was analysed scientifically via Microsoft Excel 2019, VOSviewer and R programming software. A total of 231 documents with 5321 cited references authored by 878 scholars showed that the knowledge on the studied area has been growing positively and rapidly for the past ten years. India and China are the most productive countries, comprising more than half (52%) of the total access database on the subject area in WoS. IoT application has been integrated with other knowledge domains, such as machine learning, deep learning, image processing, and artificial intelligence, to produce excellent crop and pest disease monitoring research. This study contributes to the current knowledge of the research topic and suggests possible hot topics for future direction.
... 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. ...
... 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. ...
Weeds are plants that compete for nutrients, space, and light and exert many harmful effects by reducing the quality and quantity of crops if the weed population is uncontrolled. The direct yield loss has been estimated to be within the range of 16–86%, depending on the type of rice culture, weed species, and environmental conditions. Currently, farmers apply herbicides at the same rate to control weeds. Excessive chemical usage will negatively affect the environment, crop productivity, and the economy. A map-based system can help in directing the herbicide sprayer to specific areas. Producing a weed map is very challenging due to the similarity of the crops and the weeds. Therefore, using UAVs and multispectral imagery solves the weed detection problem in a paddy field. The objective of this study project is to detect weeds in rice fields using a UAV and multispectral imagery. Multispectral imagery was used to identify the condition of the crops. It can be an indicator to determine weeds and paddy plants based on the spectral resolution in the imagery. This study was performed at Tunjang, Jitra, Kedah, which has a total area of 0.5 ha. The two types of data collections of this study are ground data and aerial data collection. Ground data were collected using the Soil Plant Analysis Development (SPAD) meter, which can read the chlorophyll value of the area. For aerial data, an unmanned aerial vehicle (UAV) was used, attached with a multispectral camera, Micasense, and a Red Green Blue (RGB) camera. Aerial data collection was conducted on the same day as ground data collection, on 30 June 2020 (the day after sowing (DAS) 34). A correlation between these two data was conducted. The study output is a weed map developed from the RGB image and multispectral imagery normalized difference vegetation index (NDVI) map. The correlation of the NDVI value with the UAV with SPAD data was weak. It has a positive, but not significant.
... 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. ...
The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes; one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and precautions to prevent further infection from occurring. Furthermore, farmers can communicate with agricultural authorities to manage their farm from home, and efficiently obtain information such as the spectral signature of crops. Therefore, the spectral signature can be used as a reference to detect pests and diseases with a hyperspectral sensor more efficiently than the conventional method, which takes more time to monitor the entire crop field. This review aims to show the current and future trends of mobile computing based on spectral signature analysis for pest and disease management. In this review, the use of mobile applications for pest and disease monitoring is evaluated based on image processing, the systems developed for pest and disease extraction, and the structure of steps outlined in developing a mobile application. Moreover, a comprehensive literature review on the utilisation of spectral signature analysis for pest and disease management is discussed. The spectral reflectance used in monitoring plant health and image processing for pest and disease diagnosis is mentioned. The review also elaborates on the integration of a spectral signature library within mobile application devices to obtain information about pests and disease in crop fields by extracting information from hyperspectral datasets. This review demonstrates the necessary scientific knowledge for visualising the spectral signature of pests and diseases using a mobile application, allowing this technology to be used in real-world agricultural settings.
... The use of mobile applications in agriculture is significant to deliver information about crops and the suitable crop management for high yield production to users. Roslin et al. (2021) developed mobile apps to monitor the crop in paddy fields for rice farming. Critical crop management includes sufficient fertilisers for specific types of crops, efficient water distribution, and irrigation scheduling. ...
The need to produce sufficient food for the Malaysian population is manifested via the National Key Economic Areas (NKEA) in the agriculture sector by the government. The NKEA focuses on the potential of growing subsectors, including a premium market for fruit and vegetables. The focus has shifted from labour to capital or technology-intensive. Therefore, automated fertigation equipped with the monitoring and control system towards a sustainable agricultural practice and higher production using the Internet of Things (IoT) such as mobile app is proposed. The system is expected to produce higher yield than the labour-based practice. The optimal amount of fertiliser and feeding time will be determined based on the data collected from the automated system. Prior to adoption of IoT, the ratio of elements required by plants involves commercial fertiliser or nutrients that are ready to use. Hence, it is crucial to develop a system that monitors all inputs of the growing environment, including pH adjusters and organic soil amendments. The proposed automated fertigation system is expected to assist farmers in optimising the amount of nutrient and water and as well as human resources needed in the farm operation.
... They found that physiological responses similar to the aerial imagery in rice field monitoring. The end user also is important to use the imagery, in which Roslim et al., [125] found that Padi2U mobile apps helps end user to get access the aerial map to monitor their field. They also used the ground data such as soil plant analysis development (SPAD) data to correlate with the multispectral imagery through the NDVI map. ...
Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.
When traditional Malaysian paddy farmers adopted the Internet of Things (IoT) in the area of Muda Agricultural Development Authority (MADA), it sparked a bioeconomy change. With regard to food security challenges around the world, researchers explored how precision farming can provide farmers with a means of improving and expanding rice production. This can be done through the bioeconomy. For paddy farmers in Malaysia, achieving cost-effective and time-efficient rice production, particularly in terms of rice quality, is a tremendous problem. We used a qualitative approach with field observations and focus group discussions. This allowed us to explore the perceptions of traditional paddy farmers in MADA about the bioeconomy, and how they could make a significant contribution when they participate. Researchers discovered that the Ministry of Agriculture encouraged farmers to adopt it. This is because the changes stemmed from the adoption of IoTs, such as drones for applying fertilizer and pesticides, but there is still a scarcity of them because traditional farmers are largely poor to participate in precision farming since drones’ usage in paddy cultivation in other countries indicated that it could help increase paddy productivity. The farmers also acknowledged and admitted their significant roles toward sustainable consumption and production patterns, both on the demand side and on the supply side of the economy.KeywordsBioeconomySocial changePaddy farmersPrecision farmingInternet of things
