Precision Agriculture Practices Research Articles

Intelligent Farming Systems in Future Using Machine Learning: A Focus on India

Abstract: The integration of machine learning (ML) into Indian agriculture holds transformative potential for addressing critical challenges such as climate variability, water scarcity, soil degradation, and market fluctuations. This paper explores the current state of ML applications in Indian agriculture, highlighting successful case studies and initiatives led by government, private sector, and academic institutions. It discusses the technological integration of ML with the Internet of Things (IoT), remote sensing, and blockchain to enhance precision farming practices. Key barriers to widespread adoption, including data quality, infrastructure, and farmer awareness, are identified, along with strategies to overcome them. Future directions emphasize the importance of robust data infrastructure, localized ML models, collaborative research, sustainable practices, and supportive policy frameworks. By leveraging ML, Indian agriculture can achieve significant improvements in productivity, sustainability, and profitability.

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

Revolutionizing Farm Management with Modern Agricultural Extension Techniques: A Review

The transformative impact of modern agricultural extension techniques on farm management in India, highlighting the integration of digital technologies, precision agriculture, and innovative management systems to enhance productivity and sustainability. Traditional farming practices in India, characterized by low efficiency and productivity, are undergoing significant changes with the introduction of technologies such as mobile apps, remote sensing, GIS, and sensor-based systems. These technologies facilitate improved crop monitoring, resource mapping, and data-driven decision-making, leading to enhanced crop yields, resource efficiency, and economic benefits. Notably, precision agriculture technologies like automated systems and IoT have enabled real-time, precision farming practices that significantly reduce resource waste and increase crop productivity. However, the adoption of these modern techniques is not without challenges. Issues such as accessibility, the digital divide, economic constraints, and the need for continuous training hinder widespread implementation. The review also discusses policy recommendations for supporting the adoption of these technologies, including increased government investment in infrastructure, regulatory frameworks, and capacity building. The integration of these modern techniques with global agricultural trends-especially those aimed at sustainability and climate change adaptation-is crucial for the future of Indian agriculture. By leveraging advanced technologies and aligning with global standards, India can not only enhance its agricultural productivity but also improve the socio-economic status of its rural population, contributing to broader economic and environmental goals.

Data annotation quality in smart farming industry

ABSTRACT Data has become paramount in modern agriculture industry, empowering precision farming practices, optimizing resource utilization, facilitating predictive analytics, and driving automation. However, the quality of data influences the usefulness of smart farming systems. Poor data quality specifically in annotations include mislabeling, inaccuracy, incompleteness, irrelevance, inconsistency, duplication, and overlap. These limitations often emerge from specific constraints, or the small scale of the problem/limited number of stakeholders, making it challenging to overcome the obstacles. This paper presents a comprehensive framework to address data annotation quality challenges. Beginning with raw, non-curated data, we integrate three strategic methodologies: active learning, enhanced annotations, and transfer learning to elevate the data quality resulting in a curated data model that achieves superior data quality refining decision-support. Leveraging a curated set of KPI, we demonstrate that integrating data quality measures leads to enhanced accuracy, reliability, and performance. Transfer learning is the most promising approach, demonstrating superior performance.

Integrating Bio-Inspired Approaches with UAV-FSN Systems for Clustered Target Area Detection in Agriculture

Abstract: Target area detection in agriculture plays a crucial role in optimizing crop management and resource allocation. Traditional methods often lack the precision and efficiency required for modern farming practices. Integrating bio-inspired approaches with UAV-FSN systems offers a promising solution to this challenge. By harnessing principles from nature, such as swarm intelligence and reinforcement learning, it becomes possible to optimize the deployment and coordination of UAVs within FSN for clustered target area detection in agriculture. This research explores the integration of bio-inspired approaches with UAV-FSN systems for clustered target area detection in agriculture. The increasing demand for precision agriculture necessitates efficient methods for identifying target areas affected by various factors. In this study, we introduce a novel algorithm, the QL-ABC Algorithm, which combines the Artificial Bee Colony (ABC) Algorithm with Q-Learning to optimize the deployment and movement of unmanned aerial vehicles (UAVs) within flying sensor networks (FSN) for target area detection. The performance outcomes of the proposed QL-ABC Algorithm on comparison with the existing algorithms are analysed using extensive simulation analysis with the appropriate simulation metrics: packet delivery ratio, mean end-to-end delay, and energy consumption. Results validate the usefulness of the proposed QL-ABC algorithm in accurately detecting clustered target areas in agricultural landscapes, thus contributing to advancements in precision farming practices and sustainable agriculture. Keywords: Flying Sensor Networks, Artificial Bee Colony Algorithm, Clustered Target Area Detection, Q-Learning, Unmanned Aerial Vehicles.

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental-based AI modeling approach

Optimal humidity control is essential for enhancing crop yields and ensuring favorable growth conditions in greenhouse agriculture. Packed bed systems have emerged as effective tools for regulating humidity levels, yet accurately assessing their performance remains unexplored, especially for temperate oceanic climates. This paper presents a packed-bed system with water as the working fluid to increase the humidity level during the winter for greenhouse cultivation. Accordingly, an experimental setup is developed, and a detailed parametric study is conducted. Further, an artificial intelligence (AI)-based modeling approach is developed for evaluating the performance of packed bed systems for greenhouse applications under varying environmental conditions with various inlet air flowrates, such as 176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr. Operating with water at an average temperature of 15.7 °C and a flow rate of 12.8 kg/min, the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda, indicating its efficiency in elevating air humidity levels. The multi-layer perceptron neural network, trained with 112 non-repeated datasets and employing a 2-10-10-1 topology, demonstrates high accuracy and resilience in estimating Δωa values for the packed bed system, with predictions closely aligning with experimental data and exhibiting a maximum discrepancy within ± 2.5%. This research contributes to the advancement of precision agriculture practices by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

Design, analysis and validation of a microstrip patch antenna with enhanced coupling for leaf moisture sensing: an IoT approach

An innovative IoT-based system utilizing a modified slotted microstrip patch antenna with enhanced coupling is presented for precise measurement of leaf moisture content. The antenna employs a rectangular slot above the feed point with an advanced coupling technique to enhance sensitivity. The antenna, fabricated on a 0.8 mm F4B substrate, is designed to resonate within the 2.40 to 3.0 GHz range under unloaded conditions. A parametric analysis focusing on leaf permittivity ranging from 20 to 30 is conducted to determine the antennas’ sensitivity. Experimental measurements of the reflection coefficient (S11) with respect to resonant frequency shift are performed with leaf samples as the samples under test (SUT). Experimental results reveal that the proposed patch antenna’s sensitivity is significantly enhanced, ranging from 0.57 to 1.67 times greater than that of traditional patch antennas for the five leaf samples tested. The antenna exhibits a sensitivity of 0.06 GHz and 0.02 GHz for the modified and enhanced coupling designs, respectively. The mean relative error between predicted and measured moisture content values is low at 0.038. The findings highlight the antenna’s increased sensitivity in detecting leaf moisture content and illustrate the potential of the proposed IoT-based system for real-time agricultural monitoring, marking an advancement in precision farming practices. The study validates the microstrip patch antenna’s capability as a moisture sensor through detailed sensitivity analysis, frequency shift measurements, and regression modeling.

Smart Agriculture Automation System using ML

Abstract: The "Smart Agriculture Automation System using Machine Learning" project envisages a future where farming transcends manual labor and embraces data-driven automation. In response to the escalating demand for food amidst a burgeoning global population, traditional farming methods are rendered insufficient. However, Smart Agriculture Automation Systems offer a transformative solution by enhancing crop production efficiency and maintaining yield quality. Climate changeinduced challenges such as erratic weather patterns and heightened pest attacks further underscore the necessity for innovation in agriculture. At the heart of this system lies real-time sensor data, enabling precision farming practices that optimize resource utilization while fostering environmental sustainability. Leveraging Machine Learning algorithms, the system not only predicts and mitigates agricultural challenges but also detects early signs of crop diseases and nutrient deficiencies. This proactive approach ensures higher yields minimizes manual tasks and minimizes wastage. The integration of IoT technology enables remote monitoring and seamless data exchange, unlocking capabilities such as precise crop prediction, weather forecasting, and fertilizer recommendations. By analyzing historical data alongside current environmental factors, these systems accurately forecast crop yields and offer insights for informed decision-making in planting and harvesting cycles. Smart Agriculture Automation Systems embody adaptability, sustainability, and accessibility, heralding a new era of intelligent and efficient modern farming. This project integrates the Internet of Things (IoT) sensors and an innovative marketplace to revolutionize the agricultural landscape. Through this system, we aim to empower farmers with data-driven insights,

An aerial framework for Multi-View grape bunch detection and route Optimization using ACO

Typically, commercial orchards and vineyards consist of large fields that encounter similar development phases at once. Thus, it becomes necessary to efficiently fly over all fields to detect fruit and identify their status in a very limited timeframe. For this purpose, Unmanned Aerial Vehicles (UAVs) path planning plays a pivotal role in agriculture as it enables optimal coverage of agricultural fields, leading to enhanced data acquisition and improved precision agriculture practices, for instance, disease assessment and pesticide application. In addition, deep learning techniques offer precise image analysis. On the one hand, object detection has been applied to agricultural fields to carry out a wide range of operations, such as detecting apples and predicting yield in vineyards, with higher detection accuracy when the fruits are fully visible. On the other hand, when crops present leaf-occlusion, the algorithms face difficulties and are unable to adapt to the specific characteristics of the field. Therefore, this study seeks to address this issue by developing a novel framework to enhance UAV path planning for data collection in vineyards, considering the current biophysical environment. To this end, the proposed framework requires two flights: i) a first flight (survey) to acquire insights on the crop structure and environment, and ii) a second flight using the Ant Colony Optimization Max-Min Ant System (ACO-MMAS) algorithm to enhance image acquisition by considering multiple angles to overcome partial leaf-occlusion. Further, the optimisation algorithm can potentially boost the acquisition of datasets for fruit detection by considering single and multiple UAVs flying synchronously while ensuring a safe distance between platforms and efficient coverage. The method was tested in two vineyards with different environmental characteristics, increasing levels of difficulty and acquired during two different growing seasons. It improved the length of the computed paths by up to 24%, compared to a base algorithm that considers only the closest point without any optimisation, improving the decision-making processes and resource allocation in crop management.

Smart Farming Robot for Detecting Environmental Conditions in a Green House

At a high level, the Smart Farming Robot for detecting environmental conditions in a greenhouse embodies a pioneering solution merging robotics and agricultural technology. This innovative system integrates advanced sensors to meticulously monitor vital parameters within the greenhouse environment, including temperature, humidity, soil moisture, light intensity, and air quality. Through seamless data collection and analysis facilitated by Raspberry Pi microcontroller technology, the robot autonomously navigates the greenhouse, continuously assessing conditions crucial for optimal plant growth. At an intermediate level, the system orchestrates a symphony of sensor data acquisition, processing algorithms, and navigational control mechanisms, all orchestrated to ensure real-time detection and response to deviations from ideal environmental conditions. Delving deeper, the system architecture encompasses intricacies of sensor interfacing, data processing, and algorithmic decision-making, harmonizing to deliver actionable insights for precision agriculture practices. Keywords- Raspberry Pico

Design and Development of an Intuitive Android Application for Smart Farming

Farmers face challenges in adapting to environmental changes, yet with decreasing implementation costs and a growing accessibility to technical expertise, progress is evident. This research introduces KisanVishwa, an Android app designed for smart farming. KisanVishwa optimizes precision agriculture systems, enabling farmers to monitor their fields in real time and address agricultural constraints. Tailored to meet the specific needs of today's farmers, the app provides user-friendly tools and the latest information on farm equipment, fertilizers, chemicals, technological advancements, and local distributors and retailers. By integrating KisanVishwa into their operations, farmers can overcome challenges associated with the adoption and maintenance of precision agriculture. KisanVishwa adopts a user-centric design for compatibility across diverse Android devices, integrating monitoring tools and collaborating with agricultural research institutions. Empowering farmers, the app addresses contemporary challenges, fostering informed decisions and efficiency. With its user-friendly interface, KisanVishwa promotes the adoption of precision agriculture, essential resources, and sustainable practices in the agricultural sector. Convincing traditional farmers to adopt advanced technologies may pose challenges. Functionality may depend on reliable internet, posing challenges in areas with limited connectivity.

Nanotechnology-based Sensors for Real-time Monitoring and Assessment of Soil Health and Quality: A Review

Soil health and quality are critical factors in maintaining sustainable agriculture, ecosystem stability, and global food security. Conventional methods for assessing soil properties are often time-consuming, labor-intensive, and lack real-time monitoring capabilities. Nanotechnology has emerged as a promising approach to develop advanced sensors for rapid, in-situ, and continuous monitoring of soil health parameters. This comprehensive review discusses the recent advancements in nanotechnology-based sensors for soil health assessment, their working principles, applications, challenges, and future prospects. We highlight the potential of various nanomaterials, such as carbon nanotubes, graphene, metal oxide nanoparticles, and quantum dots, in fabricating highly sensitive, selective, and robust soil sensors. The integration of these nanosensors with wireless communication technologies and data analytics enables real-time monitoring and precision agriculture practices. Furthermore, we discuss the environmental and ecological implications of deploying nanosensors in soil and the need for standardized protocols and regulations. This review provides valuable insights into the current state-of-the-art and future directions of nanotechnology-based sensors for soil health monitoring, promoting sustainable agriculture and environmental management.

IoT Based Crop Prediction using Machine Learning

To enhance agricultural productivity and resource allocation. Leveraging historical data encompassing various agricultural factors such as soil composition, weather conditions, and crop types, a predictive model is developed to forecast crop yields. Machine learning techniques, including regression and classification algorithms, are employed to analyze the intricate relationships within the dataset and predict the most likely outcomes for specific crops in different regions. The model is trained on a diverse dataset, considering variations in climate and soil characteristics, ensuring robustness and adaptability across different agricultural environments . The proposed crop prediction system not only provides accurate yield forecasts but also assists farmers in making informed decisions regarding crop selection and resource optimization. By harnessing the power of machine learning, this research contributes to sustainable agriculture by promoting precision farming practices. The integration of technology in crop prediction not only improves yield predictions but also supports the overall resilience of agricultural systems, fostering a data-driven approach that aligns with the evolving needs of the agricultural sector in a rapidly changing world.

Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

This research presents a comprehensive assessment of a sophisticated approach for the accurate detection and classification of various tomato leaf diseases using an improved ResNet-50 based deep learning classifier. The alarming increase in plant diseases has prompted the need for advanced technologies that can promptly identify and categorize these ailments to ensure agricultural sustainability. The proposed method harnesses the potential of deep convolutional neural networks (CNNs) and builds upon the ResNet-50 architecture, renowned for its depth and performance. However, the approach's innovation lies in the incorporation of enhancements such as advanced data augmentation techniques and transfer learning from a vast plant disease dataset. These modifications empower the model to learn intricate disease-specific features and patterns, leading to heightened accuracy and robustness. The evaluation of the approach is conducted on an extensive dataset encompassing high-resolution images of tomato leaves affected by a range of diseases. The dataset is meticulously preprocessed to ensure consistency and quality, followed by a rigorous training regimen that fine-tunes the improved ResNet-50 model.The results underscore the efficacy of the proposed method in accurate disease detection and classification. The improved ResNet-50 based classifier demonstrates exceptional performance, achieving an impressive accuracy exceeding 95%. Notably, the model showcases resilience against variations in lighting conditions, angles, and disease severity, highlighting its applicability in real-world agricultural scenarios. The implications of this research are significant, offering an efficient and reliable tool for early detection and classification of tomato leaf diseases. The integration of advanced deep learning techniques and the enhancements introduced in this work signify a substantial advancement in precision agriculture and sustainable crop management practices. As future work, this approach can be extended to address diseases in other plant species, contributing to a versatile framework that can safeguard global food production and alleviate the challenges posed by plant diseases.

Multi-Class Recognition of Soybean Leaf Diseases using a Conv-LSTM Model

This research presents an innovative approach for multi-class recognition of soybean leaf diseases using a Convolutional Long Short-Term Memory (Conv-LSTM) model. The model integrates the spatial learning capabilities of convolutional layers with the temporal dependencies of LSTM units, addressing the critical need for accurate disease detection in agriculture, particularly in soybean cultivation where leaf diseases significantly impact crop yield and quality. Through comparative experiments with established deep learning models such as AlexNet, VGG16, and ResNet50, the Conv-LSTM model demonstrates superior performance in terms of accuracy, precision, recall, and F1 score. By effectively capturing both spatial and temporal features in soybean leaf images, the Conv-LSTM model showcases its potential to enhance disease detection accuracy, supporting precision agriculture practices and enabling timely interventions to mitigate crop losses caused by diseases.

Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards

This study explores spectroscopy in the 350 to 2500 nm range for detecting powdery mildew (Erysiphe necator) in grapevine leaves, crucial for precision agriculture and sustainable vineyard management. In a controlled experimental vineyard setting, the spectral reflectance on leaves with varying infestation levels was measured using a FieldSpec 4 spectroradiometer during July and September. A detailed assessment was conducted following the guidelines recommended by the European and Mediterranean Plant Protection Organization (EPPO) to quantify the level of infestation; categorising leaves into five distinct grades based on the percentage of leaf surface area affected. Subsequently, spectral data were collected using a contact probe with a tungsten halogen bulb connected to the spectroradiometer, taking three measurements across different areas of each leaf. Partial Least Squares Regression (PLSR) analysis yielded coefficients of determination R2 = 0.74 and 0.71, and Root Mean Square Errors (RMSEs) of 12.1% and 12.9% for calibration and validation datasets, indicating high accuracy for early disease detection. Significant spectral differences were noted between healthy and infected leaves, especially around 450 nm and 700 nm for visible light, and 1050 nm, 1425 nm, 1650 nm, and 2250 nm for the near-infrared spectrum, likely due to tissue damage, chlorophyll degradation and water loss. Finally, the Powdery Mildew Vegetation Index (PMVI) was introduced, calculated as PMVI = (R755 − R675)/(R755 + R675), where R755 and R675 are the reflectances at 755 nm (NIR) and 675 nm (red), effectively estimating disease severity (R2 = 0.7). The study demonstrates that spectroscopy, combined with PMVI, provides a reliable, non-invasive method for managing powdery mildew and promoting healthier vineyards through precision agriculture practices.

Nanoparticle applications in agriculture: overview and response of plant-associated microorganisms.

Globally, food security has become a critical concern due to the rise in human population and the current climate change crisis. Usage of conventional agrochemicals to maximize crop yields has resulted in the degradation of fertile soil, environmental pollution as well as human and agroecosystem health risks. Nanotechnology in agriculture is a fast-emerging and new area of research explored to improve crop productivity and nutrient-use efficiency using nano-sized agrochemicals at lower doses than conventional agrochemicals. Nanoparticles in agriculture are applied as nanofertilizers and/or nanopesticides. Positive results have been observed in terms of plant growth when using nano-based agricultural amendments. However, their continuous application may have adverse effects on plant-associated rhizospheric and endospheric microorganisms which often play a crucial role in plant growth, nutrient uptake, and disease prevention. While research shows that the application of nanoparticles has the potential to improve plant growth and yield, their effect on the diversity and function of plant-associated microorganisms remains under-explored. This review provides an overview of plant-associated microorganisms and their functions. Additionally, it highlights the response of plant-associated microorganisms to nanoparticle application and provides insight into areas of research required to promote sustainable and precision agricultural practices that incorporate nanofertilizers and nanopesticides.

Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure

Smallholder farms are major contributors to agricultural production, food security, and socio-economic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lower-cost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.

LEAF DISEASE DETECTION AND REMEDY RECOMMENDATION

Agriculture impacts life and economic status of the people. Improper management of diseases results in annual loss of agricultural yield which will have serious effects on the quality, quantity and productivity if no proper care is taken. By using some automatic technique such as image processing, detection of leaf disease is quite significant and beneficial. The use of the most utilized deep learning classification mechanism, Convolutional Neural Network, helps in this regard. This paper proposes an innovative machine learning approach for automated leaf disease detection. By utilizing image processing and deep learning algorithms, the system analyzes leaf images taken with digital cameras or smart phones. Through training a convolutional neural network (CNN) on a comprehensive dataset containing healthy and diseased leaves, the system becomes adept at distinguishing between various disease types [1] . Leveraging tagged images of healthy and diseased leaves, our system showcases robustness and high accuracy. The automated image processing, particularly involving deep convolutional networks, ensures rapid and accurate results. The system's effectiveness will be gauged through extensive experimentation, comparing its performance against existing methods. Ultimately, this project contributes to the progress of precision agriculture and sustainable crop management practices. Key Words: Convolutional neural network (CNN), Pre- Processing, Deep Learning, Image Processing, Classification, Remedy Recommendation .

A 3D-printed hollow microneedle-based electrochemical sensing device for in situ plant health monitoring

Plant health monitoring is devised as a new concept to elucidate in situ physiological processes. The need for increased food production to nourish the growing global population is inconsistent with the dramatic impact of climate change, which hinders crop health and exacerbates plant stress. In this context, wearable sensors play a crucial role in assessing plant stress. Herein, we present a low-cost 3D-printed hollow microneedle array (HMA) patch as a sampling device coupled with biosensors based on screen-printing technology, leading to affordable analysis of biomarkers in the plant fluid of a leaf. First, a refinement of the 3D-printing method showed a tip diameter of 25.9 ± 3.7 μm with a side hole diameter on the microneedle of 228.2 ± 18.6 μm using an affordable 3D printer (<500 EUR). Notably, the HMA patch withstanded the forces exerted by thumb pressing (i.e. 20-40 N). Subsequently, the holes of the HMA enabled the fluid extraction tested in vitro and in vivo in plant leaves (i.e. 13.5 ± 1.1 μL). A paper-based sampling strategy adapted to the HMA allowed the collection of plant fluid. Finally, integrating the sampling device onto biosensors facilitated the in situ electrochemical analysis of plant health biomarkers (i.e. H2O2, glucose, and pH) and the electrochemical profiling of plants in five plant species. Overall, this electrochemical platform advances precise and versatile sensors for plant health monitoring. The wearable device can potentially improve precision farming practices, addressing the critical need for sustainable and resilient agriculture in changing environmental conditions.