Agricultural Automation Research Articles

Identification of Technical Requirements for the Initial Design of an Autonomous Fruit-Harvesting Robot

Automation in agriculture is revolutionizing the management of field tasks. This paper focuses on identifying the essential needs and preliminary technical requirements for designing an autonomous robot specifically for fruit harvesting. The study examines the integration of advanced machine vision technologies and neural networks for the accurate detection of ripe fruits and obstacles, optimizing harvesting efficiency. Key features such as proximity and touch sensors are also evaluated to ensure the safe handling of fruits, minimizing damage during harvesting. The technical specifications, including traction, speed, and the robot’s ability to operate on uneven terrain, are analyzed to ensure adaptability across various crops and ground conditions. Perception systems, incorporating cameras and sensors, are crucial for crop inspection and route planning. The main findings of this review establish a set of technical requirements that serve as a foundation for developing efficient and economically viable fruit-harvesting robots. These systems offer significant potential to transform agriculture by addressing labor shortages and improving harvesting precision. However, challenges remain, particularly in adapting to diverse crops and environments, emphasizing the need for further research and development.

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.

Enhancing detection accuracy of highly overlapping targets in agricultural imagery using IoA-SoftNMS algorithm across diverse image sizes

Object detection is crucial for agricultural monitoring and agriculture automation, yet conventional non-maximum suppression (NMS) algorithms often struggle in agricultural scenes with complex backgrounds and highly overlapping targets, especially in intersection over union (IoU) threshold setting and abnormal non-maximum removal. This study proposed the soft non-maximum suppression based on intersection over area (IoA-SoftNMS) algorithm, an advanced NMS algorithm designed to enhance detection accuracy in such challenging agricultural environments. IoA-SoftNMS employs intersection over area (IoA) to quantify overlap and integrate SoftNMS to boost overall detection precision and effectiveness. Validation experiments demonstrated that IoA-SoftNMS significantly increased detection accuracy, improving mean average precision (mAP) by 0.7 % to 3.5 % across various object detection models, without necessitating model retraining. Importantly, these enhancements barely affect the detection time. IoA-SoftNMS exhibited robust adaptability in detecting large images, especially when integrated with the slicing-aided hyper inference (SAHI) technique, where it notably improved detection accuracy during the slicing process. Application of IoA-SoftNMS elevates detection accuracy in medium (1000 × 3300 pixels) and large (2500 × 3400 pixels) images from 93 % to 96.9 % and 90.8 % to 95.3 %, respectively. These findings underscored the effectiveness of the proposed IoA-SoftNMS algorithm in improving object detection accuracy in agricultural settings and its potential to alleviate challenges posed by large image sizes, offering avenues for enhancing agricultural production precision and intelligence.

Deep Learning-Based Leafy Vegetable Image Classification for Smart Farm Automation

Deep Learning-Based Leafy Vegetable Image Classification for Smart Farm Automation

CNN-MLP-Based Configurable Robotic Arm for Smart Agriculture

Amidst escalating global populations and dwindling arable lands, enhancing agricultural productivity and sustainability is imperative. Addressing the inefficiencies of traditional agriculture, which struggles to meet the demands of large-scale production, this paper introduces a highly configurable smart agricultural robotic arm system (CARA), engineered using convolutional neural networks and multilayer perceptron. CARA integrates a highly configurable robotic arm, an image acquisition module, and a deep processing center, embodying the convergence of advanced robotics and artificial intelligence to facilitate precise and efficient agricultural tasks including harvesting, pesticide application, and crop inspection. Rigorous experimental validations confirm that the system significantly enhances operational efficiency, adapts seamlessly to diverse agricultural contexts, and bolsters the precision and sustainability of farming practices. This study not only underscores the vital role of intelligent automation in modern agriculture but also sets a precedent for future agricultural innovations.

Hybrid loading situation vehicle routing problem in the context of agricultural harvesting: A reconstructed MOEA/D with parallel populations

With the increasing level of agricultural automation, the combination of agriculture and intelligent vehicle technology is propelling the development of smart agriculture. Although this technology has already been applied widely for various agricultural production tasks, inefficient vehicle scheduling still hasn't been resolved satisfactorily. Oriented towards agricultural harvesting scenarios, a hybrid loading situation vehicle routing problem (HLSVRP) model is proposed to minimize total energy consumption and maximum completion time. A reconstructed multi-objective evolutionary algorithm based on decomposition (R-MOEA/D) is developed to solve the problem. Eight solution representations tailored specifically to the problem are introduced by R-MOEA/D, allowing an extensive exploration of the solution space. A modified Clarke & Wright (MCW) heuristic is designed to generate a high-quality initial population. A novel problem-specific parallel population updating mechanism based on the four crossover and two mutation combinations is also provided to improve the exploration ability. A collaborative search strategy is employed to facilitate cooperation among parallel populations. Finally, a series of comparative experiments conducted on various task scales and vehicle scales verify the effectiveness of the proposed algorithmic components and the exceptional performance for solving HLSVRP.

Sustainable agricultural technologies of the future: Determination of adoption readiness for different consumer groups

The rapid increase in population and unsustainable agricultural practices has significantly damaged the environment. Our study explores how to achieve food security through environmentally friendly methods such as controlled environment agriculture (CEA), genetic editing (GE), and farming automation (FA), with an emphasis on the importance of consumer acceptance of these technologies. Analyzing data from 2138 Australians and 1760 New Zealanders, we employ a two-step clustering method—(1) hierarchical clustering and (2) k-means clustering—to categorize consumers on the basis of their demographic and lifestyle preferences into three segments that capture differences in attitudes toward new agricultural technologies: “Green Urbanites,” “Environmentally Unconcerned,” and “Skeptical Foodie.” Our findings reveal that Green Urbanites are the most open to adopting CEA, GE, and FA, while Skeptical Foodies and Environmentally Unconcerned are more reluctant. We contribute to theory by studying a novel facet of consumer acceptance of sustainable technologies and revealing how consumption, living, and lifestyle patterns motivate new technology acceptance in the agricultural sector. To inform practice, we suggest tailored strategies to increase engagement of the identified segments and promote wider acceptance of sustainable agricultural practices for enhanced food security.

Enhancing Leafy Greens’ Production: Nutrient Film Technique Systems and Automation in Container-Based Vertical Farming

Urban agriculture has emerged as a crucial strategy to address food security and sustainability challenges, particularly in densely populated areas. This study focused on enhancing leafy greens’ production, specifically lettuce (Lactuca sativa L.) and arugula or rocket (Eruca sativa L.), using Nutrient Film Technique (NFT) systems and automation in container-based vertical farming. The study utilized a 20-foot shipping container retrofitted to create a thermally insulated and automated growth environment equipped with energy-efficient LED lighting and precise climate control systems. The results demonstrated significant improvements in crop yields, with the NFT systems achieving productivity up to 11 times higher than traditional methods in protected horticulture. These systems enabled continuous cultivation cycles, responding to the high market demand for fresh local produce. Moreover, the integration of low-cost sensors and automation technologies, each costing under USD 300, ensured that the environmental conditions were consistently optimal, highlighting this approach’s economic feasibility and scalability. This low-cost framework aligns with industry standards for affordable technology, making it accessible for small- to medium-sized urban agriculture enterprises. This study underscores the potential of vertical farming as a sustainable solution for urban food production. It provides a model that can be replicated and scaled to meet the growing demand for healthy, locally grown vegetables.

A Systematic Literature Review on Parameters Optimization for Smart Hydroponic Systems

Hydroponics is a soilless farming technique that has emerged as a sustainable alternative. However, new technologies such as Industry 4.0, the internet of things (IoT), and artificial intelligence are needed to keep up with issues related to economics, automation, and social challenges in hydroponics farming. One significant issue is optimizing growth parameters to identify the best conditions for growing fruits and vegetables. These parameters include pH, total dissolved solids (TDS), electrical conductivity (EC), light intensity, daily light integral (DLI), and nutrient solution/ambient temperature and humidity. To address these challenges, a systematic literature review was conducted aiming to answer research questions regarding the optimal growth parameters for leafy green vegetables and herbs and spices grown in hydroponic systems. The review selected a total of 131 papers related to indoor farming, hydroponics, and aquaponics. The review selected a total of 123 papers related to indoor farming, hydroponics, and aquaponics. The majority of the articles focused on technology description (38.5%), artificial illumination (26.2%), and nutrient solution composition/parameters (13.8%). Additionally, remaining 10.7% articles focused on the application of sensors, slope, environment and economy. This comprehensive review provides valuable information on optimized growth parameters for smart hydroponic systems and explores future prospects and the application of digital technologies in this field.

A Novel Transformer Network Based on Cross–Spatial Learning and Deformable Attention for Composite Fault Diagnosis of Agricultural Machinery Bearings

Diagnosing agricultural machinery faults is critical to agricultural automation, and identifying vibration signals from faulty bearings is important for agricultural machinery fault diagnosis and predictive maintenance. In recent years, data–driven methods based on deep learning have received much attention. Considering the roughness of the attention receptive fields in Vision Transformer and Swin Transformer, this paper proposes a Shift–Deformable Transformer (S–DT) network model with multi–attention fusion to achieve accurate diagnosis of composite faults. In this method, the vibration signal is first transformed into a time–frequency graph representation through continuous wavelet transform (CWT); secondly, dilated convolutional residual blocks and efficient attention for cross–spatial learning are used for low–level local feature enhancement. Then, the shift window and deformable attention are fused into S–D Attention, which has a more focused receptive field to learn global features accurately. Finally, the diagnosis result is obtained through the classifier. Experiments were conducted on self–collected datasets and public datasets. The results show that the proposed S–DT network performs excellently in all cases. With a slight decrease in the number of parameters, the validation accuracy improves by more than 2%, and the training network has a fast convergence period. This provides an effective solution for monitoring the efficient and stable operation of agricultural automation machinery and equipment.

Vision foundation model for agricultural applications with efficient layer aggregation network

Agricultural production is transitioning from traditional tools to IoT-connected automation devices. The integration of computer vision and agricultural automation is becoming closer with the rapid development of deep learning technology. Agricultural vision tasks, nevertheless, are relatively simple and less diverse compared to large-scale vision tasks, and there is a scarcity of plant data. This is also the reason why researchers need to make significant adjustments when directly applying advanced algorithms to the agricultural domain. In practice, downstream vision models should be designed to be concise and efficient. In this paper, we meticulously designed a deep convolutional network called TasselELANet for agricultural vision applications. It features a concise and efficient global architecture, including a 16-fold downsampling layer encoder and a decoder utilizing only 2 feature layers, which is greatly different from the general approaches. At its core is the efficient layer aggregation network, which utilizes the cross-stage fusion strategy to effectively optimize the gradient propagation path, thereby enhancing the learning ability and inference speed of the network. We validated the performance of TasselELANet on three challenging publicly available plant detection and counting datasets: maize tassels, wheat ears, and rice panels. The experimental tests achieved an average Accuracy of 0.899 and an average determination coefficient R2 of 0.867, remarkably outperforming other advanced computer vision methods in terms of accuracy and efficiency, and demonstrating sufficient generalizability. We firmly believe that TasselELANet can serve as a powerful and reliable vision tool for agricultural practitioners while also providing a solid foundation for future research. The code, datasets can be accessed at https://github.com/Ye-Sk/TasselELANet.git.

ASPEN study case: Real time in situ apples detection and characterization

Due to an increasing demand for food and pressures on our ecosystem, mechanisation and automation in agriculture has been proposed as one of the main solutions to the problems associated with overpopulation given today's life standards. To encourage the use of new technologies and bridge the gap between plant and computer science, here we validate an open-source pipeline capable of predicting real time in situ fruit characteristics, specifically in this case for apples. Using Agroscope's phenotyping tool (ASPEN), we achieve an average precision at intercept over union of 50 % of 0.75 when using YOLOv8 - m as the object detection algorithm, and with thanks to the use of multiple sensors, we find an average diameter error of 4.4 mm in the task of apple size determination. Our research demonstrates that although the pipeline tends to underestimate the actual fruit size, size estimation cannot only be used to determine the size of apples per scan, but also to track temporal apple size distribution in 4 different varieties. This research supports ASPEN in potentially replacing traditional field measurements, also suggesting that other traits could also be digitally measured for standard orchard phenotyping, either for scientific or agricultural output goals. Finally, we make publicly available a new dataset of more than 600 images (Agroscope_apple dataset) and a pre-trained model based on YOLOv8 and specifically trained for the in-situ apple detection task. By doing so, we hope to increase the accessibility and use of new technologies in the field of agriculture.

Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments

Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational information for various automated or robotic tasks such as selective harvesting and precision pruning. This study compares the one-stage YOLOv8 and the two-stage Mask R-CNN machine learning models for instance segmentation under varying orchard conditions across two datasets. Dataset 1, collected in dormant season, includes images of dormant apple trees, which were used to train multi-object segmentation models delineating tree branches and trunks. Dataset 2, collected in the early growing season, includes images of apple tree canopies with green foliage and immature (green) apples (also called fruitlet), which were used to train single-object segmentation models delineating only immature green apples. The results showed that YOLOv8 performed better than Mask R-CNN, achieving good precision and near-perfect recall across both datasets at a confidence threshold of 0.5. Specifically, for Dataset 1, YOLOv8 achieved a precision of 0.90 and a recall of 0.95 for all classes. In comparison, Mask R-CNN demonstrated a precision of 0.81 and a recall of 0.81 for the same dataset. With Dataset 2, YOLOv8 achieved a precision of 0.93 and a recall of 0.97. Mask R-CNN, in this single-class scenario, achieved a precision of 0.85 and a recall of 0.88. Additionally, the inference times for YOLOv8 were 10.9 ms for multi-class segmentation (Dataset 1) and 7.8 ms for single-class segmentation (Dataset 2), compared to 15.6 ms and 12.8 ms achieved by Mask R-CNN's, respectively. These findings show YOLOv8's superior accuracy and efficiency in machine learning applications compared to two-stage models, specifically Mask-R-CNN, which suggests its suitability in developing smart and automated orchard operations, particularly when real-time applications are necessary in such cases as robotic harvesting and robotic immature green fruit thinning.

Status of Farm Automation, Advances, Trends, and Scope in India

Status of Farm Automation, Advances, Trends, and Scope in India

Students Opinions of the Faculty of Agriculture, Cairo University Related to Automation of Agriculture

Students Opinions of the Faculty of Agriculture, Cairo University Related to Automation of Agriculture

Improved YOLO-FastestV2 wheat spike detection model based on a multi-stage attention mechanism with a LightFPN detection head.

The number of wheat spikes has an important influence on wheat yield, and the rapid and accurate detection of wheat spike numbers is of great significance for wheat yield estimation and food security. Computer vision and machine learning have been widely studied as potential alternatives to human detection. However, models with high accuracy are computationally intensive and time consuming, and lightweight models tend to have lower precision. To address these concerns, YOLO-FastestV2 was selected as the base model for the comprehensive study and analysis of wheat sheaf detection. In this study, we constructed a wheat target detection dataset comprising 11,451 images and 496,974 bounding boxes. The dataset for this study was constructed based on the Global Wheat Detection Dataset and the Wheat Sheaf Detection Dataset, which was published by PP Flying Paddle. We selected three attention mechanisms, Large Separable Kernel Attention (LSKA), Efficient Channel Attention (ECA), and Efficient Multi-Scale Attention (EMA), to enhance the feature extraction capability of the backbone network and improve the accuracy of the underlying model. First, the attention mechanism was added after the base and output phases of the backbone network. Second, the attention mechanism that further improved the model accuracy after the base and output phases was selected to construct the model with a two-phase added attention mechanism. On the other hand, we constructed SimLightFPN to improve the model accuracy by introducing SimConv to improve the LightFPN module. The results of the study showed that the YOLO-FastestV2-SimLightFPN-ECA-EMA hybrid model, which incorporates the ECA attention mechanism in the base stage and introduces the EMA attention mechanism and the combination of SimLightFPN modules in the output stage, has the best overall performance. The accuracy of the model was P=83.91%, R=78.35%, AP= 81.52%, and F1 = 81.03%, and it ranked first in the GPI (0.84) in the overall evaluation. The research examines the deployment of wheat ear detection and counting models on devices with constrained resources, delivering novel solutions for the evolution of agricultural automation and precision agriculture.

Smart Agriculture: A Comprehensive Survey on IoT-Enabled Plant Disease Detection and Agricultural Automation

This research paper is dedicated to the comprehensive review and discussion of diverse techniques employed in plant disease detection within the realm of agriculture. Emphasizing notable contributions and showcasing innovative methodologies, the research work takes a critical turn to address the myriad issues and challenges intricately woven into the integration of IoT data analytics in agriculture. The paper meticulously unravels the complexities associated with plant disease detection in the era dominated by IoT and data analytics. Serving as more than just a repository of current methodologies and technologies, this work actively illuminates the challenges that await further exploration. The insights derived from this exploration will provide a substantial foundation for emerging researchers. By shedding light on the evolving landscape of plant disease detection and the nuances of IoT integration in agriculture, this paper empowers researchers to actively contribute to the resilience and sustainability of agricultural practices in the face of ongoing challenges.

IoT Enabled Mushroom Farm Automation with Machine Learning

Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.

Smart irrigation with crop recommendation using machine learning approach

Increasing crop yield with sustainable growth is the primary requirement for farmers with a growing population. Effective management and conservation of depleting natural resources is a priority task. Decrease in manpower due to migrating population has forced automation in agriculture. In this work, an automatic water irrigation and an effective crop recommendation system is proposed. Gypsum blocks based soil sensor is used to measure dielectric permittivity associated with the tested soil. The water-potential present in soil, along with potassium (K), nitrogen (N), phosphorus (P), potential of hydrogen (pH) helps to quantify the soil nutrients available and the suitable crop that can be considered for harvesting in a specified demography and environment. Sensory data indicating soil quality obtained is used to recommend crops by utilizing machine learning approaches. Telegram application is linked to the recommendation model to assist decision making and to ensure farmer-friendliness by sending notifications periodically.

Design, Fabrication and Monitoring of Automated Polyhouse

The agriculture sector faces the challenge of feeding a growing population while dealing with the effects of climate change. With the limited expansion of cropland, agriculture automation has become necessary. Internet of Things (IoT) and Artificial Intelligence (AI) have made significant strides in various industries, including agriculture. These digital technologies offer smart systems that can monitor, control, and visualize farm operations in realtime, approaching human expertise. The potential applications of IoT and AI in agriculture include smart machinery, irrigation systems, pest control, crop monitoring, and more. Automated polyhouses equipped with IoT and AI components can optimize crop conditions, enhancing yield and quality while minimizing climate impacts. Over the past decade, research in this field has matured, transitioning from conceptual stages to implementation. The integration of AI and IoT into agriculture practices poses technical challenges, but understanding these technologies will facilitate their adoption and the development of IoT and AI-based solutions in farming.