Precision Agriculture Research Articles

Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet’s lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.

An adaptive hexagonal deployment model for resilient wireless sensor networks in precision agriculture

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol’s performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network’s robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol’s exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol’s prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications.

Advancements in smart agriculture through innovative weed management using wavelet-based convolution neural network

Smart agriculture has shifted the paradigm by integrating advanced technologies, particularly weed management. This paper introduces an innovative approach to weed control by applying a Wavelet-based Convolution Neural Network (WCNN). In the era of precision agriculture, our study explores the integration of WCNN into real-world scenarios, emphasizing its adaptability to diverse environmental conditions. Utilizing the spatial-frequency analysis features of wavelets and convolutional neural networks, the WCNN model is the most effective at finding weeds, classifying them, and managing them specifically in agricultural fields in real-time. This research contributes to the scientific discourse on smart agriculture and addresses the challenges of invasive weeds, presenting a sustainable solution for optimizing resource utilization. Our investigation includes a detailed exploration of WCNN’s adaptive learning mechanisms and dynamic adjustment to changing agricultural landscapes. The model seamlessly integrates with existing smart farming infrastructure, showcasing a substantial reduction in manual intervention and a simultaneous increase in agricultural productivity. We incorporate fog computing and resource optimization into our framework, enhancing the efficiency of onboard data processing. To evaluate the real-world efficacy of WCNN, we conducted comprehensive experiments in texture classification and image labelling using two distinct datasets: the plant seedling and soybean weed datasets. Results demonstrate the superior performance of WCNN, achieving higher accuracy in training and test scenarios with significantly fewer parameters than traditional CNNs. For the soybean weed dataset, WCNN achieved remarkable accuracy in the training (0.9970) and testing (0.9987) phases, with correspondingly low losses of 0.0109 and 0.0048. The WCNN model demonstrated high accuracy during training (0.9739) and testing (0.9902), with minimal losses of 0.0898 and 0.0239 in the plant seedling dataset.

Hybrid Wind‐Solar Self‐Powered Detector System Using a Triboelectric‐Photovoltaic Generator for Smart Agriculture

The growth of crops is affected by factors such as temperature, humidity, and salinity, so it is critical to monitor these indicators through detectors in smart agriculture. Collecting renewable energy in the farmland environment to power the detector will provide a feasible approach. Therefore, a triboelectric‐photovoltaic hybrid generator (WS‐TPHG) is proposed to collect solar and wind energy efficiently. The generator structure includes a photovoltaic panel (PV) and a wind‐driven triboelectric nanogenerator (W‐TENG). The switching function of the power supply can be realized through the power management circuit. In the case of sufficient light, the detector is powered by PV steadily, and in the case of insufficient light, the detector is powered by W‐TENG via the switching function. The PV can supply a stable voltage of 8.1 V, and the open‐circuit voltage (VOC) of W‐TENG can reach 478.6 V at 180 rpm. The results verify that the detection of temperature, humidity, and salinity can be successfully achieved through the detector which is powered by WS‐TPHG. In this article, the potential application of WS‐TPHG is demonstrated in farmland to provide a solution for the sustainable development of agriculture.

The Benefits of the Adoption of Smart Agriculture in Oil Palm Estates: A Qualitative Study

Palm oil is an incredibly efficient crop because it is a natural, renewable resource with multiple uses. It is used in food, cosmetics and even biofuel. It is used in food products, detergents, cosmetics and even biofuel. The palm oil industry employs millions of people and is an essential source of income for many developing countries. Smart agriculture is the future for Malaysian oil palm plantations via the adoption of new technology. It will provide a solid foundation for precision agriculture to increase productivity in oil palm production. The purpose of this study is to discover the benefits of smart agriculture in oil palm estates in Malaysia. Semi-structured interviews were conducted to obtain an in-depth understanding of the benefits of the technology in oil palm estate. Interviews were conducted with a total of twelve estate managers and assistant managers of oil palm estates in Malaysia. The interview was transcribed and analysed using thematic analysis. Based on the findings, the participant mentioned that the use of smart agriculture technology has helped the estate in terms of increasing efficiency, reducing labour use and saving time. Therefore, with these transformative technologies, the oil palm industry will continue to thrive to ensure that this industry remains viable and sustainable.

Progress and Potential Drawbacks of Modern Agricultural Technologies: A Literature Review

The production of agriculture has undergone new modifications as a result of agricultural technologies. These not only boost agricultural output but may also significantly raise the caliber of produced food, cut labor expenses, boost farmers' incomes, and achieve agricultural modernization. The use of precision agriculture (PA) is expanding due to the rapid socioeconomic changes that are occurring in certain developing nations. There are enormous ramifications for urbanization, energy consumption, and economic growth in certain developing nations when fundamental changes occur. The research status and current agricultural technology achievements are carefully summarized in this study. In-depth discussions of thirteen significant agricultural technologies are provided in this article. All significant technologies from developed countries are discussed so that under-developed and lower-developed countries will benefit from this paper. Finally, some fresh concepts for each technology are offered, and potential issues in establishing such sophisticated technologies are identified. The main objective of this review is to increase knowledge of modern agriculture and the development process in the agricultural field.

Optimizing Olive Harvesting Efficiency Through UAVs and AI Integration

Olive harvesting, integral to global agriculture, faces challenges with traditional gathering methods which are labor-intensive and limited in precision, highlighting the need for innovative approaches. The study explores the application of Unmanned Aerial Vehicles (UAVs) in olive harvesting, with the aim to enhance efficiency and sustainability. Traditional methods, employing wood rakes, sticks, and rudimentary mechanical devices, pose challenges with high labor intensity and potential tree damage. UAVs present a transformative solution, offering aerial surveying, high-resolution imaging, and harvest timing data collection. This study evaluates existing olive harvest technologies, illuminating the superior efficiency of UAVs in minimizing damage and maximizing yield. The Potohar region in Pakistan exemplifies one area of intensive olive production, emphasizing the need for precision Agri-techniques. This study introduces the conceptual design of olive harvesting using drones, demonstrating its potential to revolutionize traditional practices, provide real-time monitoring, and enhance environmental sustainability. Results indicate substantial reductions in non-harvestable fruit and wood damage with drone-based harvesting, underscoring its promise for increased yield and reduced environmental damage. In conclusion, this study advocates for the adoption of precision agriculture techniques, integrating Artificial Intelligence and aerial technologies, to propel the olive industry's productivity and environmental impact forward.

Challenges of making a new farmer: a case study of Thailand’s Young Smart Farmer program

ABSTRACT In the recent years, there has been a global decline in the number of farmers contributing to the agrarian transition. Therefore, this study delves into the case study of Thailand's ‘Young Smart Farmer (YSF)’ program, designed to counteract the declining farmer population. The study seeks to elucidate the motivations behind individuals opting to become new farmers and the challenges they face. It revealed five primary motivations to embrace farming: profitability, well-being, traditional and cultural values, taking over the family business, and social influences. This study also identifies the current challenges encountered by Thai farmers, including issues related to agricultural production, marketing, assets, seasonality and unexpected shocks, and criticism. To improve the program’s effectiveness, this study recommends four key strategies: establishing knowledge certification linkages, refinement of workshops and training content, expanding farmer networks, and implementing policies aimed at nurturing new farming talent.

Design and testing of a microwave Doppler-based yield estimation system for machine-picked seed cotton

Abstract Accurately obtaining crop yields is an important part of the precision agriculture technology system; with the increased mechanisation of cotton planting and harvesting, it has become particularly important to accurately obtain seed cotton yield data. In order to achieve accurate estimation of machine-picked seed cotton yield, this paper designs a machine-picked seed cotton yield estimation system based on microwave Doppler principle. Based on LabVIEW software, the signal acquisition circuit is designed; the power spectrum of the echo signal is estimated; the signal echo power is obtained; the relationship model between echo power and seed cotton mass is established by using multiple regression equations; and the reliability of the model is verified by using statistical methods. By adjusting the rotational speed of the fan, the wind speed at the inlet of the cotton pipeline was 10 m s−1, 15 m s−1 and 20 m s−1, and the yield estimation tests were carried out at the three wind speeds, and the data showed that the average absolute percentage errors of the estimation at the three wind speeds were 7.36%, 7.72% and 8.17%, and the mean squared errors were 2.699, 4.938 and 4.026, respectively. The test results show that the accuracy of seed cotton mass estimation under the three wind speeds basically meets the needs of the yield measurement system, and the systematic error of seed cotton yield estimation is the smallest when the wind speed is 10 m s−1.

A bibliometric analysis of smallholder farmers’ climate change adaptation challenges: a SADC region outlook

Purpose This paper aims to explore pathways in which adaptation challenges may occur. Focus is on the barriers to adaptation, challenges to adaptation and maladaptation with reference to smallholder farmers in the Southern African Development Community region. Design/methodology/approach Bibliometric analysis techniques were used to track the literature on smallholder farmers’ adaptation challenges. Web of Science was the main data source. A total of 41 articles were retained for analysis and exported into Visualization of Similarities Viewer Software where the development of research on the subject, co-occurrence of keywords analysis, top publishers, citations and total link strength was done. Findings Results indicate that research on smallholder farmers’ adaptation challenges is not new but has gained more consideration post-2020. The main adaptation challenges emanate from perception barriers and constraints based on determinants of adoption, limitations for resilience building and achieving sustainable adaptation as well as contestations around Climate Smart Agriculture technologies. Practical implications Effective design of adaptation policies should center on prioritizing the needs of the local people. This would reduce the occurrences of smallholder farmers’ adaptation challenges, promote resilience building and contribute toward achieving sustainable adaptation. Originality/value It is equally important to document adaptation challenges. However, adaptation challenges are rarely shared with the same enthusiasm as its successes. This work focuses on the matter with the intention of conscientizing smallholder farmers to reduce the risk of repeating the same adaptation mistakes.

Lightweight Detection of Broccoli Heads in Complex Field Environments Based on LBDC-YOLO

Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices.

APD-YOLOv7: Enhancing Sustainable Farming through Precise Identification of Agricultural Pests and Diseases Using a Novel Diagonal Difference Ratio IOU Loss

The diversity and complexity of the agricultural environment pose significant challenges for the collection of pest and disease data. Additionally, pest and disease datasets often suffer from uneven distribution in quantity and inconsistent annotation standards. Enhancing the accuracy of pest and disease recognition remains a challenge for existing models. We constructed a representative agricultural pest and disease dataset, FIP6Set, through a combination of field photography and web scraping. This dataset encapsulates key issues encountered in existing agricultural pest and disease datasets. Referencing existing bounding box regression (BBR) loss functions, we reconsidered their geometric features and proposed a novel bounding box similarity comparison metric, DDRIoU, suited to the characteristics of agricultural pest and disease datasets. By integrating the focal loss concept with the DDRIoU loss, we derived a new loss function, namely Focal-DDRIoU loss. Furthermore, we modified the network structure of YOLOV7 by embedding the MobileViTv3 module. Consequently, we introduced a model specifically designed for agricultural pest and disease detection in precision agriculture. We conducted performance evaluations on the FIP6Set dataset using mAP75 as the evaluation metric. Experimental results demonstrate that the Focal-DDRIoU loss achieves improvements of 1.12%, 1.24%, 1.04%, and 1.50% compared to the GIoU, DIoU, CIoU, and EIoU losses, respectively. When employing the GIoU, DIoU, CIoU, EIoU, and Focal-DDRIoU loss functions, the adjusted network structure showed enhancements of 0.68%, 0.68%, 0.78%, 0.60%, and 0.56%, respectively, compared to the original YOLOv7. Furthermore, the proposed model outperformed the mainstream YOLOv7 and YOLOv5 models by 1.86% and 1.60%, respectively. The superior performance of the proposed model in detecting agricultural pests and diseases directly contributes to reducing pesticide misuse, preventing large-scale pest and disease outbreaks, and ultimately enhancing crop yields. These outcomes strongly support the promotion of sustainable agricultural development.

A Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture.

Object Detection Algorithm for Citrus Fruits Based on Improved YOLOv5 Model

To address the challenges of missed and false detections in citrus fruit detection caused by environmental factors such as leaf occlusion, fruit overlap, and variations in natural light in hilly and mountainous orchards, this paper proposes a citrus detection model based on an improved YOLOv5 algorithm. By introducing receptive field convolutions with full 3D weights (RFCF), the model overcomes the issue of parameter sharing in convolution operations, enhancing detection accuracy. A focused linear attention (FLA) module is incorporated to improve the expressive power of the self-attention mechanism while maintaining computational efficiency. Additionally, anchor boxes were re-clustered based on the shape characteristics of target objects, and the boundary box loss function was improved to Foal-EIoU, boosting the model’s localization ability. Experiments conducted on a citrus fruit dataset labeled using LabelImg, collected from hilly and mountainous areas, showed a detection precision of 95.83% and a mean average precision (mAP) of 79.68%. This research not only significantly improves detection performance in complex environments but also provides crucial data support for precision tasks such as orchard localization and intelligent picking, demonstrating strong potential for practical applications in smart agriculture.

Reinforcement learning-based assimilation of the WOFOST crop model

Crop model assimilation is a crucial technique for improving the accuracy and precision of crop models by integrating observational data and crop models. Although conventional data assimilation methods such as Kalman filtering and variational methods have been widely applied, these methods often face limitations in data quality, model bias, and high computational complexity. This study explored the potential of reinforcement learning (RL) for crop model assimilation, which has the advantage of not requiring large datasets. Based on the WOFOST crop model, two RL environments were constructed: a Daily-Data Driven approach and a Time-Series Driven approach. The Proximal Policy Optimization (PPO) algorithm was used to train these environments for 100,000 iterations. The assimilation results were compared with the commonly used SUBPLEX optimization algorithm using four-year field measurement data and a public dataset with added random errors. Our results demonstrate that the Time-Series Driven RL model achieved assimilation accuracy comparable to the SUBPLEX optimization algorithm, with an average MAE of 0.65 compared to 0.76 for SUBPLEX, and a slight decrease in RMSE, while significantly reducing the computational burden by 365 times. In a multi-year stability test, the Time-Series Driven RL model and SUBPLEX had similar assimilation performance. This study demonstrates the potential of RL for crop model assimilation, providing a novel approach to overcome the limitations of conventional assimilation algorithms. The findings suggest that RL-based crop model assimilation can improve model accuracy and efficiency, with potential for practical applications in precision agriculture.

Self-healable and stretchable perovskite-elastomer gas-solid triboelectric nanogenerator for gesture recognition and gripper sensing

Gas-solid triboelectric nanogenerators (GS-TENGs) based on porous elastomers provide a promising avenue for the design of self-powered sensors. However, the intrinsic limitation of low electric output in GS-TENGs could affect the accuracy and sensitivity of sensing systems. Here, we develop a porous composite by integrating an adhesive poly(siloxane-diphenylglyoxime-urethane) (PSDU) elastomer with ferroelectric (3,3-difluorocyclobutylammonium) 2 CuCl 4 [(DF-CBA) 2 CuCl 4 ] fillers. PSDU, an intrinsically triboelectric negative material with alternating hard-soft segments and supramolecular bonds, imparts excellent compressibility, adhesion, and self-healing properties to the composite. Simultaneously, the incorporation of (DF-CBA) 2 CuCl 4 as functional fillers leverages the formation of a hydrogen bonding network to enhance charge transfer processes. These fillers facilitate charge accumulation through an electric poling process, resulting in a power output improvement that surpasses 1400 times higher than that of a dense PSDU-based GS-TENG. Tapping onto the versatile properties of porous poly(siloxane-diphenylglyoxime-urethane)-perovskite (PSDU-PK) GS-TENGs, applications such as hand gesture/food recognition and dual-mode sensing system have been demonstrated, suggesting their promising potential in wearable electronics and smart agriculture.

Design and Development of Automated IoT-Aided Smart Agriculture Management System for Efficient Crop Growth Using Hybrid Convolution (1D–2D)-Based Adaptive Residual Attention

Design and Development of Automated IoT-Aided Smart Agriculture Management System for Efficient Crop Growth Using Hybrid Convolution (1D–2D)-Based Adaptive Residual Attention

Quadcopters in Smart Agriculture: Applications and Modelling

Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters’ use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, “quadcopters”, and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters’ use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments.

Automation and AI in Precision Agriculture: Innovations for Enhanced Crop Management and Sustainability

Precision agriculture is one of the ways to achieve food security and sustainability through better resource-use optimization and crop productivity dealing with the challenges posed by the growing population and addressing environmental concerns. The study offers an in-depth look at the most recent developments in artificial intelligence (AI) and automation in precision agriculture (PA), with a particular emphasis on important technologies such as drones, autonomous tractors, AI-driven irrigation systems, and predictive analytics for crop management. The accuracy of crop monitoring and health assessments has increased by 30–50 percent as a result of AI-powered solutions, which have improved resource-based decision-making. Systems for precision irrigation and fertilization have increased crop yields by 5–15 percent when using 25–40 percent less water and 30-40 percent less fertilizer, respectively. Robotic harvesters and sprayers are examples of automation technologies that have reduced labor expenses by 20–40 percent and increased operational efficiency by 35 percent. Additionally, AI-based prediction models have reduced pest damage by 20–25 percent and reached an accuracy of 85–90 percent for crop yield forecasts and pest control. Despite these developments, issues of scalability, affordability for small farms, and data privacy still exist, which can hinder technology adoption among farmers. The evaluation follows by outlining ideas for future research, such as 5G, blockchain, and AI integration with cloud and edge computing. These technologies could improve decision-making and transparency in precision agriculture by enabling real-time data transmission, secure data management, and enhanced traceability, thus addressing current limitations and fostering trust among stakeholders.

Defective Pennywort Leaf Detection Using Machine Vision and Mask R-CNN Model

Demand and market value for pennywort largely depend on the quality of the leaves, which can be affected by various ambient environment or fertigation variables during cultivation. Although early detection of defects in pennywort leaves would enable growers to take quick action, conventional manual detection is laborious and time consuming as well as subjective. Therefore, the objective of this study was to develop an automatic leaf defect detection algorithm for pennywort plants grown under controlled environment conditions, using machine vision and deep learning techniques. Leaf images were captured from pennywort plants grown in an ebb-and-flow hydroponic system under fluorescent light conditions in a controlled plant factory environment. Physically or biologically damaged leaves (e.g., curled, creased, discolored, misshapen, or brown spotted) were classified as defective leaves. Images were annotated using an online tool, and Mask R-CNN models were implemented with the integrated attention mechanisms, convolutional block attention module (CBAM) and coordinate attention (CA) and compared for improved image feature extraction. Transfer learning was employed to train the model with a smaller dataset, effectively reducing processing time. The improved models demonstrated significant advancements in accuracy and precision, with the CA-augmented model achieving the highest metrics, including a mean average precision (mAP) of 0.931 and an accuracy of 0.937. These enhancements enabled more precise localization and classification of leaf defects, outperforming the baseline Mask R-CNN model in complex visual recognition tasks. The final model was robust, effectively distinguishing defective leaves in challenging scenarios, making it highly suitable for applications in precision agriculture. Future research can build on this modeling framework, exploring additional variables to identify specific leaf abnormalities at earlier growth stages, which is crucial for production quality assurance.