3.1. Articles reviewed
In this research, a total of 39 articles were retrieved out of the 423 articles searched using the keywords (Table 3). A comprehensive article categorization strategy was employed to systematically organize a diverse set of studies focused on agricultural innovations. The categorization strategy was structured based on three key dimensions: the type of technology/technique utilized the agricultural application area, and the purpose of innovation. Under the dimension of technology/technique, articles were grouped as "Precision agriculture," encompassing innovations related to advanced farming methodologies and studies leveraging technologies and platforms. The agricultural application area dimension facilitated the grouping of articles into categories like "Sustainable farming practices," highlighting innovations contributing to environmentally conscious and sustainable farming methods, "Novel materials/equipment," about studies introducing new materials or equipment in agriculture, and "Image analysis," focusing on innovations utilizing image processing techniques. Lastly, the purpose of the innovation dimension featured the "Decision support systems" category, specifically targeting innovations geared towards enhancing analytics, monitoring, and decision-making processes in farming.
3.2. Distribution of agricultural innovation categories in reviewed articles
The reviewed articles (n = 39) regarding agricultural innovations in the Philippines were categorized into five broad domains - precision technology, sustainable farming practices, novel materials/equipment, image analysis, and decision support systems (Fig. 2). Image analysis represented the largest share with 26.00% of articles, encompassing innovations utilizing imaging techniques and analytics for applications like crop monitoring and disease detection. This highlights advanced cameras, sensors, and AI-enabled image recognition as key emerging technologies suited for Philippine agriculture. Sustainable farming practices had the next highest share at 23.00%, pointing to the prominence of techniques like conservation agriculture and integrated pest management for improving environmental sustainability. Sustainable farming is indeed a significant focus of agricultural innovation. Together, technology-driven analytical innovations and sustainable agricultural practices comprise almost half of the identified innovations, indicating these as high-potential areas for the future of Philippine agriculture based on the discourse in the reviewed literature. The remaining half includes decision support systems (21.00%), precision technology (15.00% of articles), and novel materials/equipment (15.00%). The distribution shows emphases on cutting-edge technological innovations utilizing imagery, sensors, and computing as well as sustainable practices for reduced environmental impact. This highlights the twin goals of increasing efficiency and productivity alongside ecological agricultural stewardship. As all categories claim a reasonable share of attention, it suggests agricultural innovation efforts in the Philippines span a diverse range of complementary approaches. Image analysis is one of the significant A.I. tools used in agriculture innovation (Susheel et al., 2023). For instance, image processing, machine learning, and deep learning are used for disease identification in crops (Haq et al., 2023). Weed detection in wheat crops is also done using image analysis and artificial intelligence. Additionally, hyperspectral image analysis is used in crop yield and biomass estimation (Li et al., 2022). Additionally, research has shown that sustainable agricultural innovation is essential for enhancing the sustainable agricultural value chain and promoting systematic overhaul in the agriculture sector (Singh & Srivastava, 2021). Furthermore, agricultural innovation is also regarded as an important aspect of the shift to more sustainable and robust farming systems globally (Grovermann et al., 2018). There are several initiatives including responsible agricultural mechanization innovation and the development of gender-specific programming (Devkota et al., 2020; Benítez et al., 2020).
3.3. Distribution of agricultural innovation across crop and animal types
Figure 3 shows the distribution of agricultural innovations across different crop and animal types reviewed in the articles. Rice takes the lead, with innovations targeting lowland, upland, and traditional rice farming constituting 33.33% of the total. This huge focus on rice is unsurprising given it is a staple crop and the top agricultural product of the Philippines. The need for yield improvements amid land constraints and climate threats is driving research interest in rice. Next are vegetables at 17.94%, as their rising demand makes horticultural efficiency critical. Innovations for other key crops like banana, coffee, and mango have very low shares, indicating research gaps for these crops. Livestock innovations make up 7.69%, despite the growth potential of the meat industry. This signals the need for more studies on the sizable fisheries and poultry sectors. Though most Filipinos derive livelihood from farms, only 7.69% of innovations target upland sectors, showcasing imbalanced representation across commodities.
Table 3
Agricultural innovations of the retrieved
Innovation | Purpose | Source |
1 | Unmanned aerial sprayer | Aerial spray system for agricultural applications | Agurob et al., 2023 |
2 | UAV-based multispectral vegetation monitors | Vegetation indices and leaf color chart observations | Bacsa et al., 2019 |
3 | Laser-controlled land leveling | Laser-controlled land leveling in rice production | Nguyen-Van-Hung et al., 2022 |
4 | Soil moisture sensor system | Wireless soil moisture sensor for precision agriculture | Cruz et al., 2018 |
5 | Drone-based GIS Mapping | Drone-based GIS Mapping of Cassava Pythoplasma Disease | Plata et al., 2022 |
6 | Wireless sensor technology and GPS | Low-cost, portable IoT dashboard for smart farming. | Santos et al., 2019 |
7 | Integrated rice-duck farming | Ducks feed on pests, and weeds, and fertilize rice. | Baldo & Laureta, 2022 |
8 | SCoPSA as a sustainable and viable farming method | SCoPSA, contour farming, hedgerows, double-row planting, agro-waste utilization | Sabado et al., 2021 |
9 | Alternate wetting and drying technology | AWD technology improves Agusan soil rice cultivation | Magahud et al., 2019 |
10 | Indigenous knowledge systems and practices for managing natural environments | Natural environment management, soil erosion control, and productivity approaches. | Gomez Jr, 2020 |
11 | Conversion of rice husks into biochar | Biochar, RHB soil amendment, nano silica synthesis. | Sarong et al., 2020 |
12 | Development of SAKAHANDA | SAKAHANDA: Android app for farmers and Municipal Agriculture Office | Batoon et al., 2023 |
13 | Linear programming for cost minimization of feeds | Linear programming minimizes feed costs | Borlas et al., 2021 |
14 | Smart greenhouse system | Smart greenhouse: Arduino control, GSM, SMS notifications for environment management | Elenzano, 2021 |
15 | Vertical Farming using Hydroponic Technology | Hydroponics for onion, focus on acceptability and viability. | Pascual et al., 2018 |
16 | IoT-enabled systems and multiple regression | IoT, regression predict frost in highland crops | Mendez & Dasig, 2020 |
17 | Infrared thermography for grading rough rice | Precision agriculture: ICT, infrared thermography, Smart Sensor AR991 | Bejarin & Fajardo, 2023 |
18 | Local unmanned aerial vehicle (UAV) pesticide sprayer | UAV pesticide sprayer for rice fields | De Padua et al., 2021 |
19 | Soil sensors via a Wireless Sensor Network | IoT system links environmental, soil sensors via WSN | Lorilla & Cabaluna et al., 2023 |
20 | Wireless water level sensors | Wireless water level sensors for AWD irrigation management | Pereira et al., 2022 |
21 | Nano fertilizer called FertiGroe | Nano fertilizer called FertiGroe for banana | Augustus & Domingo 2023 |
22 | Vision-based velocity estimation combined | For accurate spot spraying without auxiliary velocity measurement | Sanchez & Zhang, 2023 |
23 | Support Vector Machine classifier and CIELab color space | For automatic tomato ripeness identification | Garcia et al., 2019 |
24 | GPS, sensors, and data analytics to optimize agricultural practices | GPS, sensors, and data analytics to optimize lettuce production | Lauguico et al., 2020 |
25 | Aerial vision-based proximal sensing with a low-altitude UAV | To estimate weed and pest damage in eggplant | de Ocampo and Dadios, 2021 |
26 | Artificial bee colony-optimized visible oblique dipyramid greenness index | For accurate estimation of lettuce crop parameters using images | Concepcion II et al., 2023 |
27 | Computer application for identifying and determining mango pests | Iidentifying and determining mango pests using images | Rocha IV and Lagarteja, 2020 |
28 | Automatic identification for Abaca Bunchy Top Disease | Automatic identification for Abaca Bunchy Top Disease | Patayon & Crisostomo, 2021 |
29 | Potassium nanofertilizer using kappa-carrageenan | Potassium nanofertilizer using kappa-carrageenan as a carrier | Toledo et al., 2019 |
30 | Integration of sensor applications, data analysis, and cloud-based data centers | IoT technology for automated estrus detection | Arago et al., 2022 |
31 | IoT and Machine Learning for monitoring plants | Monitoring coffee plants nutritional deficiencies | Espineli and Lewis, 2021 |
32 | Multi-temporal Synthetic Aperture Radar TerraSAR-X and Sentinel-1 | Rice area mapping and determine the Start of Season (SoS) | Gutierrez et al., 2019 |
33 | Weather-Rice-Nutrient Integrated Decision Support System (WeRise). | Accuracy of the Weather-Rice-Nutrient Integrated Decision Support System | Hayashi et al., 2021 |
34 | Deficit irrigation as a water-saving management strategy | water-saving management strategy for corn production | Painagan & Ella, 2022 |
35 | Wireless sensor technology and GPS | Low-cost, portable cloud-based smart farming system | Santos et al., 2019 |
36 | Arduino-based automated data acquisition system | Automated data acquisition system for hydroponic farming | Tagle et al., 2018 |
37 | Agrinex, a low-cost wireless mesh-based smart irrigation | Low-cost wireless mesh-based smart irrigation | Tiglao et al., 2020 |
38 | GIS-based land suitability model | Model for selecting agricultural tractors in lowland rice ecology | Amongo et al., 2023 |
39 | Prototype design of a smart irrigation system using Internet of Things (IoT) | Internet of Things (IoT) for monitoring a vegetable farm | Velasco, 2020 |
20.51% of studies did not specify any crop, making geographical extrapolation difficult. The pieces of information display a disproportionate focus on rice compared to other equally important agricultural commodities of the Philippines. This calls for diversification of research efforts, with more innovations directed at high-value crops, livestock/poultry, coconut, and upland farmers for balanced and inclusive growth.
3.4. Geographic representation of agricultural innovation in the Philippines
The geographical representation of agricultural innovations reviewed in the articles is presented in Fig. 4. An overwhelming majority of 69.23% of innovations target Luzon, especially central and northern areas. This skew is unsurprising as Luzon is the country's rice bowl and economic center. Luzon, particularly the northern part, is known for its fertile land and suitable climate, which make it an ideal region for rice cultivation (Quimba & 2021). This region is home to a significant portion of the Philippines' rice production, and the agricultural sector plays a crucial role in the local economy.
However, very few studies focus on the central and southern islands of Visayas (2.56%) and Mindanao (10.25%), which are also major agricultural hubs renowned for exports of tropical fruits, vegetables, and seafood (Estigoy et al., 2022). For 17.94% of articles, the geographical context is unspecified, hampering the localization of findings. This showcases a heavy fragmentation in research efforts, with a lack of emphasis on key farming regions beyond Luzon. As the Philippines strives towards food security and agricultural competitiveness, growth opportunities abound in the fertile lands and coasts of Visayas and Mindanao as well. More balanced representation covering their unique challenges, traditional practices, terrain suitability, and local farmer needs is imperative. It will ensure comprehensive development of the sector across crops, technologies, and geographies - especially tapping the potential of smallholder tribal communities dependent on agri-livelihoods in remote southern areas. Unified nationwide strategies for innovation transfer and capacity building should also accompany studies for wider impact.
3.5. Key implementers
Table 4 highlights the key contributors to agricultural innovations in the reviewed articles. The University of the Philippines Los Baños leads with a 12.8% share, underlining the critical role of academic research in advancing the sector. Multiple universities across Luzon are actively innovating for regional farming needs as well, though most are based in and around Metro Manila. Research institutes like IRRI and the Philippine Rice Research Institute focus specifically on rice sector issues. However, very few studies originate from universities in Visayas and Mindanao, echoing the geographical fragmentation observed earlier. Moreover, individual shares of contributors are small, with most at 2.5-5% only. This showcases the disjointed efforts plaguing the agricultural innovation landscape - with insufficient cross-institutional partnerships, fragmented solutions, and lack of coordination impeding large-scale development or adoption after initial pilots.
Table 4
Key implementers of agricultural innovations in the Philippines
Key implementers | N = 39 | Percentage |
AMA University Quezon City, Philippines | 2 | 5.1 |
Batangas State University, Batangas | 1 | 2.5 |
Benguet State University, Philippines | 1 | 2.5 |
Bulacan State University | 1 | 2.5 |
Central Luzon State University, Nueva Ecija, Philippines | 1 | 2.5 |
De La Salle University, Manila, Philippines | 3 | 7.6 |
Department of Agriculture Regional Field Office No. 02, | 1 | 2.5 |
Far Eastern University Manila, Philippines | 1 | 2.5 |
International Rice Research Institute (IRRI), Laguna | 2 | 5.1 |
Isabela State University, Isabela | 4 | 10.2 |
Japan International Research Center for Agricultural Sciences | 1 | 2.5 |
Jose Rizal Memorial State University, Zamboanga del Norte | 1 | 2.5 |
LORMA Colleges, San Fernando, La Union | 1 | 2.5 |
Mapua University, Manila, Philippines | 1 | 2.5 |
Mindanao State University – Iligan Institute of Technology | 1 | 2.5 |
Nueva Ecija University of Science and Technology, Cabanatuan, Philippines | 1 | 2.5 |
Partido State University, Camarines Sur, Philippines | 1 | 2.5 |
Philippine Rice Research Institute, Agusan del Norte, Philippines | 1 | 2.5 |
Samar State University, Catbalogan City, Philippines | 1 | 2.5 |
Tarlac Agricultural University, Philippines | 1 | 2.5 |
Technological Institute of the Philippines – Quezon City | 1 | 2.5 |
Technological University of the Philippines, Manila, PHILIPPINES | 1 | 2.5 |
University of Science and Technology of Southern Philippines | 1 | 2.5 |
University of Southern Mindanao, Kabacan | 1 | 2.5 |
University of the Philippines Diliman | 2 | 5.1 |
University of the Philippines Los Baños, Laguna, Philippines | 5 | 12.8 |
Visayas State University, Leyte | 1 | 2.5 |
For true sectoral impact, the unification of innovation ecosystems is critical via Industry-Academia partnerships, technology transfer conduits, and nationwide farming extension services. Applied research answering grassroots-level needs should be prioritized over theoretical studies. Most importantly, capacity building of end beneficiaries i.e. small and marginal farmers through financial, infrastructure, and skill development is crucial for them to utilize innovative solutions, as currently sparse adoption levels indicate unpreparedness. Hence consolidated, farmer-centric strategies are vital to shaping agricultural innovations into truly meaningful and widespread change agents.
3.6. Precision agriculture
Precision agriculture refers to farming management concepts that utilize technological tools to enhance agricultural productivity and efficiency (Cruz et al., 2018). Emerging technologies such as sensors, robots, drones, satellite imagery, and information technology allow farmers to improve decision-making in crop production (Santos et al., 2019). Precision agriculture can involve practices like variable rate technology, automated equipment guidance systems, remote sensing, and specialized information management tools (Bacsa et al., 2019). When effectively implemented, precision agriculture enables farmers to use inputs more efficiently, reduce environmental impact, increase productivity, and boost profitability (Nguyen-Van-Hung et al., 2022). Recent literature on precision agriculture technologies and practices in the Philippines highlights innovative applications across a range of crop production systems. For example, Plata et al. (2022) developed a drone-based mapping system using geospatial data analysis to detect cassava diseases. Cruz et al. (2018) designed a wireless sensor network that monitors soil moisture content to aid water management. Bacsa et al. (2019) utilized multispectral data from drones to assess crop nutrient status and guide fertilizer application. These studies demonstrate the potential of emerging technologies to enhance agricultural sustainability through more targeted and efficient input management based on real-time monitoring of crop growth conditions. However, barriers such as high upfront costs, lack of technical knowledge, challenges in data analysis, and absence of policy incentives can hinder the wide-scale adoption of precision agriculture (Agurob et al., 2023). More research and field testing is needed to validate benefits and support integration into existing production systems across diverse contexts in the Philippines (Nguyen-Van-Hung et al., 2022). As precision agriculture solutions become more accessible and tailored to local conditions, they can play a vital role in improving the productivity and resilience of Philippine agriculture amidst climate change impacts and resource constraints.
3.7. Sustainable farming practices
The reviewed studies highlight several sustainable farming practices that can enhance crop productivity while preserving environmental resources. Integrated rice-duck farming (IRDF) allows ducks to feed on pests and weeds in rice paddies while fertilizing plants, increasing rice productivity (Baldo & Laureta, 2022). Contour farming, planting hedgerows, and implementing the sustainable corn production in sloping areas (SCoPSA) framework reduced soil erosion by 63% and increased corn yield by 70% (Sabado et al., 2021). Alternate wetting and drying (AWD) technology for rice cultivation improved soil properties and plant growth compared to continuous flooding (Magahud et al., 2019). The studies also demonstrate the potential of agricultural waste products. Rice husk biochar increases soil nutrients and plant biomass in degraded upland soil (Sarong et al., 2020). Vertical hydroponic farming of onions using rice husk substrates resulted in significantly higher bulb growth compared to field cultivation (Pascual et al., 2018). Nanosilica and mushroom compost derived from rice husks and other crop residues offer additional income streams for farmers (Sarong et al., 2020; Sabado et al., 2021). Several studies emphasize the role of technology in promoting sustainable agriculture through precision monitoring and control of growing environments (Elenzano et al., 2021; Batoon et al., 2023) and optimizing productivity and costs (Borlas et al., 2021). However, scaling out these innovations requires technical support and initial investment subsidies, indicating a role for governments and organizations in facilitating adoption by smallholder farmers (Pascual et al., 2018; Baldo & Laureta, 2022). The reviewed literature demonstrates that sustainable intensification of smallholder farming is indeed achievable through integrated pest management approaches, efficient water and nutrient cycling practices, waste valorization techniques, and precision agriculture technologies. Further research should focus on adapting these farming solutions to local biophysical and socioeconomic contexts across diverse agricultural systems and agroecological regions.
3.8. Novel materials/equipment
Recent agricultural innovations in the Philippines have focused on developing novel materials and equipment to improve crop management practices. These include Internet of Things (IoT)-enabled systems, infrared thermography technologies, unmanned aerial vehicles, wireless sensor networks, nano fertilizers, and water level sensors. Several studies have utilized IoT systems connected to sensors to monitor microclimate conditions and predict frost events (Mendez & Dasig, 2020), drought conditions (Lorilla & Cabaluna et al., 2023), and automate irrigation scheduling (Augustus & Domingo, 2023). For example, Mendez and Dasig (2020) developed an IoT-based highland crop management system using multiple regression models to forecast frost risk. The system transmitted real-time sensor data on temperature, humidity, and precipitation to a web platform and provided SMS alerts to farmers on predicted frost events. Infrared thermography technologies are also emerging for agricultural applications such as non-destructive evaluation of crop quality. Bejarin and Fajardo (2023) demonstrated the use of infrared thermography for detecting moisture content and impurities in rough rice samples. The technique was over 87% accurate for moisture detection and 95% accurate for impurity detection compared to standard methods. The non-contact nature of infrared thermography allows rapid inspection without damaging harvest samples. Several studies have also evaluated the potential of unmanned aerial vehicles (UAVs) to improve the efficiency of routine agricultural tasks. De Padua et al. (2021) developed an automated hexacopter UAV with remote controls and autopilot capabilities for aerial pesticide application over rice paddies. The UAV had a tank capacity of 1 L and could cover 750 m^2 in 10 minutes at an application rate of 3.2 L per 1000 m^2. Further optimization of battery life and durability is required before large-scale adoption. Nonetheless, the unit cost was favorable compared to commercial UAV sprayers. Wireless sensor networks coupled with IoT connectivity have also emerged as an important tool for real-time monitoring of soil conditions and irrigation management. Lorilla and Cabaluna et al. (2023) designed a smart irrigation system using long-range wireless sensors to transmit soil moisture and temperature data to a central device. The system used neural network algorithms for the data-driven triggering of solenoid valves controlling water flow to the field. A mobile app also allowed remote monitoring of sensor data for early disease detection and preventative irrigation scheduling. Several studies also evaluated nano fertilizers' effect on crop growth and yield. Augustus and Domingo (2023) tested a nano nitrogen-phosphorus-potassium fertilizer called FertiGroTM on banana plants over 12 weeks. They found that soil application of the nano fertilizer led to significantly better plant growth and development compared to foliar spray application. The slow-release property of the nano fertilizer makes it suitable for direct soil application to improve nutrient absorption. The technology shows good potential to reduce fertilizer use and nutrient loss in Philippine banana plantations. Finally, Pereira et al. (2022) developed and tested submersible water level sensors for measuring flood height in lowland rice fields under alternate wetting and drying irrigation regimes. They found that sensor accuracy was significantly affected by water turbidity. However, calibration equations could account for turbidity levels up to 4300 FAU with an overall variance explanation > 99%. The improved sensor can help better regulate water usage in water-scarce regions to improve irrigation efficiency.
3.9. Image analysis
Recent agricultural innovations in the Philippines have increasingly incorporated image analysis techniques to enable real-time and accurate monitoring and assessment of crops. Specifically, machine vision and computer vision methods have been applied for various precision agriculture goals including growth tracking, disease detection, yield forecasting, and selective treatment. Sanchez and Zhang (2023) developed a deep learning-based machine vision system to estimate the velocity of a precision sprayer system by tracking the relative motion of crops. This velocity estimate successfully guided variable time delay queuing and dynamic filtering to achieve precise spraying without needing additional velocity measurement instrumentation. In another pest management application, Rocha and Lagarteja (2020) designed a computer application that employed convolutional neural networks (CNN) for automated identification and classification of mango pests using smartphone images. The CNN model demonstrated a high accuracy of 88.75% on the test dataset of images. Beyond pest and disease detection, image analysis has also shown promise for tracking crop growth parameters. Garcia et al. (2019) applied support vector machines on RGB images to classify tomato ripeness into six graded categories with 83.39% accuracy. Such capability can better inform harvest timing and reduce crop spoilage due to premature or delayed picking. For monitoring plantation health, de Ocampo and Dadios (2021) used aerial images captured by unmanned aerial vehicles to detect weeds and estimate pest damage in eggplant farms. Their sub-image classification methodology achieved a 97.73% F1 score in isolating crops from the background. Concepcion et al. (2023) introduced a novel greenness index computed from common smartphone images to estimate various lettuce crop parameters including weight, height, leaf count, and growth stage. Strong linear correlations were demonstrated between the index and ground truth measurements of these parameters. The innovation and success of these image analysis techniques underline the potential of computer vision and machine learning methods to enable automated, rapid, non-invasive assessment of crop status. Precision agriculture stands to benefit immensely from such capabilities through data-driven decision-making for diverse tasks like yield forecasting, growth monitoring, and selective treatment. As Arago et al. (2022) demonstrated in their smart dairy farming system, combining image analysis with IoT technology can also enable intelligent remote monitoring solutions. Further testing across more crop types, agricultural environments, and farm sizes would be beneficial to assess wider adoption. Additionally, exploring more advanced neural networks and image processing algorithms as well as fusing multiple data sources could further optimize accuracy and expand practical applications.
3.10. Decision support systems
Decision support systems (DSS) refer to integrated computer-based platforms that collect, analyze, and interpret data to aid users in making well-informed decisions across various domains (Hayashi et al., 2021; Velasco, 2020). In the context of agriculture, a DSS typically consists of components such as sensors, IoT modules, satellite systems, analytical engines, crop simulation models, and user interfaces (Tiglao et al., 2020; Tagle et al., 2018). These systems draw on multiple data sources related to weather, soil conditions, water availability, and crop growth stages (Santos et al., 2019; Gutierrez et al., 2019). Through algorithms, predictive models, and data visualization, agricultural DSS provides actionable advisories to farmers on ideal planting dates, real-time irrigation requirements, fertilizer needs, and other farming activities tailored to the specific crop variety, geography, soil health and climatic factors (Painagan & Ella, 2022). Various studies have explored the development and application of decision support systems to enhance agricultural productivity and efficiency in the Philippines. For instance, Hayashi et al. (2021) evaluated the Weather-Rice-Nutrient Integrated Decision Support System (WeRise) which integrates seasonal climate prediction and crop models to provide advisories on optimal sowing dates and varieties for rainfed rice farmers. Field testing showed higher grain yields when using the WeRise system compared to farmers’ traditional practices. Additionally, Gutierrez et al. (2019) utilized multi-temporal SAR imagery and rule-based models to map rice areas and determine optimal planting windows based on water levels and vegetation growth cycles. Their model strongly correlated with actual farmer-reported planting dates. Other sensor-based decision support systems include the automated hydroponics monitoring tool developed by Tagle et al. (2018) and the smart irrigation prototype by Tiglao et al. (2020) which used wireless sensors in a mesh network to monitor moisture and automate watering. These systems increased efficiency in resource use and crop yields. Velasco (2020) also prototyped a solar-powered smart irrigation system using IoT technology and showed its potential for improving agricultural productivity through precise monitoring and control of irrigation. Furthermore, Santos et al. (2019) developed a cloud-based decision support dashboard to provide real-time analytics on crop production suitability based on weather, soil conditions, and location. By integrating wireless sensor data and GPS mapping, their system enabled evidence-based planning for enhanced productivity and yield. Overall, these studies demonstrate that advanced decision support systems, especially those incorporating ICT and precision agriculture techniques, can play a vital role in improving agricultural practices, optimizing resource utilization, increasing farmer incomes, and contributing to food security in the Philippines. Further verification across diverse contexts and integration with emerging technologies can help strengthen these solutions.