Agricultural Tasks Research Articles

Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare.

Abstract 3438: Associations between estimated endotoxin exposure and circulating immunological markers among male farmers

Abstract Occupational exposure to endotoxin has been associated with reduced lung cancer risk. Farmers who perform certain tasks (e.g., raising hogs) can be highly exposed to endotoxin. The mechanisms through which endotoxin could confer a lower risk of lung cancer, while likely to be immune mediated, are not clear. To address this gap, we evaluated associations between estimated endotoxin exposure and circulating immunologic markers among male farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. Our investigation included 122 non-smoking farmers from Iowa, oversampling those raising hogs (n=61). We evaluated 58 serologic markers, including some linked to lung cancer, that were measured using multiplex bead-based assays. Based on an algorithm linking personal air measurements in BEEA and published data to reported farming tasks, we estimated cumulative endotoxin exposure in the last 30 days and 12 months before sample collection. We used multivariable linear regression to estimate geometric mean ratios of immune markers across exposure quartiles, overall and by season of sample collection. Higher endotoxin exposure in the last 30 days was associated with increased levels of FGF-2, MIP-3A/CCL20, and sIL-4R (Ptrend ≤ 0.02), and with decreased levels of MDC/CCL22 (Ptrend = 0.02). In analyses stratified by season, the positive associations with FGF-2 and MIP-3A/CCL20 were most apparent in the winter (Ptrend = 0.002 and 0.03, respectively); we also observed an inverse association with sCD27 levels in the summer (Ptrend = 0.03). Similar patterns were observed for endotoxin exposure in the last 12 months. This study is the first to use novel algorithm-based endotoxin exposure estimates to evaluate associations with intermediate lung cancer-related markers. Several markers were associated with endotoxin levels in an exposure-response manner. Our findings suggest possible biological mechanisms through which endotoxin may reduce risk of lung cancer. Table 1. Associations between estimated endotoxin exposure in the last 30 days and selected serum im Citation Format: Somayina C. Ezennia, Laura E. Beane-Freeman, Vicky C. Chang, Shuai Xie, Gabriella Andreotti, Melissa C. Friesen, Jonathan N. Hofmann. Associations between estimated endotoxin exposure and circulating immunological markers among male farmers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3438.

Intelligent vineyard blade density measurement method incorporating a lightweight vision transformer

Under the new demand model of Agriculture 4.0, automated spraying is a very complex task in precision agriculture, which needs to be combined with a computerized vision perception system to distinguish the plant leaf density and execute the spraying operation in real-time accordingly. Aiming at the accurate determination of grape leaf density, an image leaf density determination method based on the lightweight Vision Transformer (ViT) architecture is proposed, which designs a fusion data augmentation method containing a dual augmentation spatial extension and weather data augmentation method, where the former adopts the pixel augmentation and spatial augmentation for the original image processing, and the latter realizes the data augmentation from the empirical point of view adapted to the agricultural operation environment, and fuses the two in order to expand the sample capacity of the grape leaf density image, which then enhances the model's generalization ability and robustness. The lightweight ViT model has self-attention that can automatically and efficiently extract high-frequency local feature representations and use the two-branch structure to mix high-frequency and low-frequency information to form grapevine-leaf density features in the region of interest. The semantic analysis of the feature extraction layer is parsed using t-SNE and histogram methods, which improves the transparency of the model from the multidimensional with frequency domain distribution space. The experimental results show that the fusion data augmentation method can effectively improve the model recognition accuracy, and the accuracy of comparing the included data augmentation methods is improved by 0.55 % and 3.46 %, respectively. The accuracy of recognizing all four types of grape leaf densities exceeded 94 %, and the MCC reached 90.39 %. In addition, the proposed lightweight ViT improves the accuracy by at least 0.34 % with FLOPs of only 0.6 G compared to the popular MobileViT. The proposed method of this work has high recognition speed and accuracy, which can provide practical technical support for plant protection spraying robots and improve the profitability of growers based on the reduction of pesticide residues.

Smart Agri Bot for Multi-Crop Harvesting

The integration of automation and robots into modern agriculture is crucial for improving output and efficiency. A recent development in this field is the creation of a color-sensing robot specifically designed for agricultural purposes. This robot utilizes advanced color detection technology to accurately identify objects and exert control over them based on their color characteristics. The primary goal of this research is to find solutions for labor-intensive agricultural tasks, particularly fruit and vegetable harvesting. By combining robotics and computer vision technology, this robot aims to streamline these processes. Automation plays a significant role in reducing human effort in routine and frequently performed tasks. In the case of agriculture, color-sensing robots have been employed to pluck fruits and vegetables. The robot's mobile application is controlled through a Bluetooth Module, sending instructions to the microcontroller, and then to the motor. The robot's architecture is also programmed with Arduino, an open-source programming language that is a simplified version of other languages. These robots have the potential to reduce damage, increase productivity, and prove cost- effective, while also freeing up labor resources. In addition to object recognition, the robot's navigational capabilities have been enhanced with the integration of an ultrasonic sensor. This sensor emits high-frequency sound waves and analyzes the echoes to accurately estimate distances to nearby objects. This information is crucial for the robot to identify obstacles in its path, ensuring safe and effective navigation

Stem–Leaf segmentation and phenotypic trait extraction of individual plant using a precise and efficient point cloud segmentation network

Rapid and precise 3D organ segmentation is crucial for the automatic extraction of phenotypic traits, forming a fundamental prerequisite for intelligent plant breeding. The advancement of deep learning technology has replaced labor-intensive manual measurements and traditional computer vision methods, which are sensitive to parameters, in phenotypic trait extraction. However, current larger network structures not only require extensive point cloud data but also consume substantial computational resources, rendering them unsuitable for agricultural tasks with limited plant samples. Therefore, this study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction. The adopted simple-but-effective network architecture and innovative modern operations, including a high-dimensional feature mapping strategy for preprocessing input points, a local feature extraction module based on inverted residual bottleneck block, and a cost-free attention block for spatial feature fusion, effectively implement multi-scale hierarchies and adaptively reduce computational overheads. Experimental results from cotton stem-leaf segmentation demonstrated that PEPNet not only presented approximately 2 × faster inference speed (9.59 ms) and throughput (146.32 plants per second) but also achieved competitive segmentation performance compared to other six state-of-the-art deep learning networks, namely PointNet++, DGCNN, CurveNet, Point Cloud Transformer, PointMLP, and SPoTr, achieving 95.99 %, 94.66 %, 95.32 %, and 91.31 % in Precision, Recall, F1-score, and mIoU, respectively. In transferability experiments with tomato and soybean plants, PEPNet achieved almost all the best metrics and significantly outperformed the second-best model (CurveNet). Furthermore, ablation study verified the optimal trade-off between efficiency and accuracy in this network. Any modifications to the modules could potentially disrupt the optimal trade-off. This work could contribute to reducing computational resources and annotation costs for applying segmentation methods in high-throughput phenotyping tasks.

Assessment of an arm-support exoskeleton on physical demands, task performance, and usability during simulated agricultural tasks

This laboratory-based study aimed to determine whether an arm-support exoskeleton (ASE) would be an effective intervention to reduce the physical strain associated with manual agricultural work. Twenty-four (gender-balanced) participants performed pruning and harvesting tasks (at four different heights: knee, elbow, shoulder, and overhead), lifting/lowering, and Timed Up & Go (TUG) tasks with and without an ASE. During these tasks, muscle activity (in the upper trapezius, anterior deltoid, biceps brachii, and erector spinae), task completion time, perceived exertion, and usability were assessed. The results indicated a significant reduction (31.7%–60.2%) in muscle activity, particularly in the upper trapezius and anterior deltoid, when using the ASE during tasks at shoulder work height or above. However, there was an observed increase in muscle strain in the erector spinae, suggesting potential risk to the lower back. Given these findings, a more rigorous evaluation of ASEs in agricultural tasks should be pursued before implementing exoskeletons in agricultural tasks to avoid unintended health hazards. Relevance to industryA relatively new application of exoskeleton technology, extensively studied in fields like rehabilitation, manufacturing, and the military, is its use in agriculture. This study details the physical requirements of specific tasks to offer insights into the challenges that exoskeleton technologies for agriculture may encounter.

Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure

In the last decade, digital twin (DT) technology has received considerable attention across various domains, such as manufacturing, smart healthcare, and smart cities. The digital twin represents a digital representation of a physical entity, object, system, or process. Although it is relatively new in the agricultural domain, it has gained increasing attention recently. Recent reviews of DTs show that this technology has the potential to revolutionise agriculture management and activities. It can also provide numerous benefits to all agricultural stakeholders, including farmers, agronomists, researchers, and others, in terms of making decisions on various agricultural processes. In smart crop farming, DTs help simulate various farming tasks like irrigation, fertilisation, nutrient management, and pest control, as well as access real-time data and guide farmers through ‘what-if’ scenarios. By utilising the latest technologies, such as cloud–fog–edge computing, multi-agent systems, and the semantic web, farmers can access real-time data and analytics. This enables them to make accurate decisions about optimising their processes and improving efficiency. This paper presents a proposed architectural framework for DTs, exploring various potential application scenarios that integrate this architecture. It also analyses the benefits and challenges of implementing this technology in agricultural environments. Additionally, we investigate how cloud–fog–edge computing contributes to developing decentralised, real-time systems essential for effective management and monitoring in agriculture.

A fast hyperspectral change detection algorithm for agricultural crops based on low-rank matrix and morphological feature extraction

Crop change detection study is the foundation of agricultural sustainability. The inherent high spectral resolution of hyperspectral images, combined with multi-temporal datasets, facilitates the detection of subtle changes. To enhance the accuracy and applicability of hyperspectral change detection in agricultural scenes, this paper introduces a fast hyperspectral change detection approach for agricultural crops based on low-rank matrix and morphological feature extraction (FLRaMF). The goal is to improve detection precision and computational efficiency of the change detection process. The method initially employs rapid low-rank matrix extraction to separate changing and non-changing pixels in the spectral domain. Subsequently, spatial information is introduced using attribute profiles, restricting spectral anomalies through hyperspectral morphology, which ultimately improves the detection results. This study utilized four hyperspectral change detection datasets in agricultural crop scenarios, optimizing and analyzing parameters. Experimental results and analysis indicate that the FLRaMF method can achieve higher detection accuracy with reduced computation cost in unsupervised, default parameter scenarios when performing agricultural crop change detection tasks.

Toward Virtual Testing of Unmanned Aerial Spraying Systems Operating in Vineyards

In recent times, the objective of reducing the environmental impact of the agricultural industry has led to the mechanization of the sector. One of the consequences of this is the everyday increasing use of Unmanned Aerial Systems (UAS) for different tasks in agriculture, such as spraying operations, mapping, or diagnostics, among others. Aerial spraying presents an inherent problem associated with the drift of small droplets caused by their entrainment in vortical structures such as tip vortices produced at the tip of rotors and wings. This problem is aggravated by other dynamic physical phenomena associated with the actual spray operation, such as liquid sloshing in the tank, GPS inaccuracies, wind gusts, and autopilot corrections, among others. This work focuses on analyzing the impact of nozzle position and liquid sloshing on droplet deposition through numerical modeling. To achieve this, the paper presents a novel six degrees of freedom numerical model of a DJI Matrice 600 equipped with a spray system. The spray is modeled using Lagrangian particles and the liquid sloshing is modeled with an interface-capturing method known as Volume of Fluid (VOF) approach. The model is tested in a spraying operation at a constant velocity of 2 m/s in a virtual vineyard. The maneuver is achieved using a PID controller that drives the angular rates of the rotors. This spraying mission simulator was used to obtain insights into optimal nozzle selection and positioning by quantifying the amount of droplet deposition.

Human-exoskeleton cooperation for reducing the musculoskeletal load of manual handling tasks in orchid farms

Work-related musculoskeletal diseases of the lower back and shoulders are frequently caused by lifting, carrying and trunk bending. Therefore, a hybrid exoskeleton (HExo) that provides external torque to the upper extremities and back may reduce discomfort in the lower back and shoulder tendinopathies. In this study, we designed and fabricated HExo, a novel upper-limb and lower-back combined exoskeleton, and evaluated its impact in reducing the musculoskeletal load on the shoulder and back during agricultural work in orchid farms. The human motion tracking technique was utilised to collect the joint angles of orchid farmers during their work, and the joint angle data were subsequently used for the design of HExo. Electromyography (EMG) and centre of pressure (CoP) measurements were taken for 5 participants performing farm tasks and 16 participants performing laboratory-standardised tasks both with and without the HExo. The amplitude probability density function and Wilcoxon signed rank test were utilised in the data analysis to verify the advantage in wearing HExo. The use of HExo resulted in a reduction in erector spinae, latissimus dorsi and anterior deltoid muscle activation of up to 36.92 % (p = 0.05), 31.43 % (p = 0.017), and 57.59 % (p = 0.012), respectively. Furthermore, its use did not affect the ability of participants to maintain balance. Hence, HExo could be a promising for reducing solution musculoskeletal load.

ResNet101-SVM: Hybrid convolutional neural network for citrus fruits classification

Recognizing and classifying citrus fruits is a challenging yet crucial task for agriculture, food processing, and quality control. Classifying citrus fruits is challenging because of their wide variety, often with a similar flesh appearance, shape, and size. Therefore, efficient and effective approaches are required for accurate identification. Our study focused on efficiently identifying citrus fruit types by utilizing a hybrid ResNet101-SVM model. ResNet101-SVM is the combination of the feature extraction capabilities of the ResNet101 with the classification power of SVM. This hybrid approach leverages the strengths of both deep learning (feature extraction) and traditional machine learning (SVM classification) to improve the accuracy and robustness of citrus fruit classification. The model outperformed the standard ResNet101 model across various performance metrics, achieving impressive accuracy, sensitivity, specificity, precision, F1 Score, MCC, and Kappa values of 99.81%, 99.81%, 99.8%, 99.82%, 0.18%, 99.81%, 99.80%, and 98.77%, respectively. This study holds significant promise for various applications, particularly in the domains of food processing and quality control.

Sex differences in mechanical stress and associated labor in Hellenistic–early Roman Menainon, Sicily

AbstractThis paper explores mechanical stress in the Hellenistic/early Roman community of Menainon (Sicily) to test the existence of sex‐based division of labor. For this purpose, human skeletal remains from 79 males and 65 females, dating to the 4th–1st ca. BCE, were examined for degenerative joint disease, intervertebral disc disease, cross‐sectional geometric properties, and entheseal changes. Our findings support an equal share of workload between males and females, who appeared to have experienced similar mechanical stress levels. In contrast, they do not support a systematically greater mechanical load among males, as would have been expected if females had been restricted indoors taking care of domestic tasks only, while men had dealt with most physically demanding outdoor activities. The patterns identified in the current study agree with a scenario where agriculture was a prominent subsistence activity (as attested historically for this region) and most members of this rural community engaged in agricultural tasks irrespective of sex. Our study has limitations regarding the available historical and archaeological information, which restrict our ability to formulate solid research hypotheses or fully contextualize the bioarchaeological results. Nonetheless, it does highlight the importance of critically assessing historical information regarding the role of men and women in the Greco‐Roman society.

Exploring inclusion in UK agricultural robotics development: who, how, and why?

The global agricultural sector faces a significant number of challenges for a sustainable future, and one of the tools proposed to address these challenges is the use of automation in agriculture. In particular, robotic systems for agricultural tasks are being designed, tested, and increasingly commercialised in many countries. Much touted as an environmentally beneficial technology with the ability to improve data management and reduce the use of chemical inputs while improving yields and addressing labour shortages, agricultural robotics also presents a number of potential ethical challenges – including rural unemployment, the amplification of economic and digital inequalities, and entrenching unsustainable farming practices. As such, development is not uncontroversial, and there have been calls for a responsible approach to their innovation that integrates more substantive inclusion into development processes. This study investigates current approaches to participation and inclusion amongst United Kingdom (UK) agricultural robotics developers. Through semi-structured interviews with key members of the UK agricultural robotics sector, we analyse the stakeholder engagement currently integrated into development processes. We explore who is included, how inclusion is done, and what the inclusion is done for. We reflect on how these findings align with the current literature on stakeholder inclusion in agricultural technology development, and suggest what they could mean for the development of more substantive responsible innovation in agricultural robotics.

Comparing pre-trained models for efficient leaf disease detection: a study on custom CNN

Leaf disease detection is a crucial task in modern agriculture, aiding in early diagnosis and prevention of crop infections. In this research paper, authors present a comprehensive study comparing nine widely used pre-trained models, namely DenseNet201, EfficientNetB3, EfficientNetB4, InceptionResNetV2, MobileNetV2, ResNet50, ResNet152, VGG16, and Xception, with our newly developed custom CNN (Convolutional Neural Network) for leaf disease detection. The objective is to determine if our custom CNN can match the performance of these established pre-trained models while maintaining superior efficiency. The authors trained and fine-tuned each pre-trained model and our custom CNN on a large dataset of labeled leaf images, covering various diseases and healthy states. Subsequently, the authors evaluated the models using standard metrics, including accuracy, precision, recall, and F1-score, to gauge their overall performance. Additionally, the authors analyzed computational efficiency regarding training time and memory consumption. Surprisingly, our results indicate that the custom CNN performs comparable to the pre-trained models, despite their sophisticated architectures and extensive pre-training on massive datasets. Moreover, our custom CNN demonstrates superior efficiency, outperforming the pre-trained models regarding training speed and memory requirements. These findings highlight the potential of custom CNN architectures for leaf disease detection tasks, offering a compelling alternative to the commonly used pre-trained models. The efficiency gains achieved by our custom CNN can be beneficial in resource-constrained environments, enabling faster inference and deployment of leaf disease detection systems. Overall, our research contributes to the advancement of agricultural technology by presenting a robust and efficient solution for the early detection of leaf diseases, thereby aiding in crop protection and yield enhancement.

A Method of Detection and Identification for Axillary Buds

During the period from sowing and planting to harvesting, outdoor crops are directly affected by the natural environment, including wind, rain, frost, and sunlight. Under such circumstances, vegetables change their growth conditions, shape, and flexibility daily. We aimed to develop an agricultural work-support robot that automates monitoring, cultivation, disease detection, and treatment. In recent years, many researchers and venture companies have developed agricultural harvesting robots. In this study, instead of focusing on intensive harvesting operations, we focused on daily farm operations from the beginning of cultivation to immediately before harvest. Therefore, gripping and cutting are considered basic functions that are common to several routine agricultural tasks. To find the assumed objects from a camera image with a low computational load, this study focuses on branch points to detect and identify even if the stems, lateral branches, and axillary buds are swaying in the wind. A branch point is a characteristic part close to the working position, even when the wind blows. Therefore, we propose a method to detect the assumed branch points simultaneously and divide each branch point into the main stem, lateral branch, and axillary bud. The effectiveness of this method is demonstrated through experimental evaluations using three types of vegetables, regardless of whether their stems are swaying.

An Overview of Rural Credit through Public Sector Banks in Karnataka- A Study of State Bank of India in Hassan District

Credit is not only one of the critical inputs in agriculture but also an effective means of overall development of rural economy. A number of agencies such as co-operatives, regional rural banks, commercial banks, self- help groups and a well spread informal credits out lets together constitutes the Indian rural credit delivery system. Rural credit is any type of lending program or line of credit that is aimed at impacting a rural population in some manner. There are banks and co operatives that specialize in extending these types of credit to farmers and others engaged in the agricultural task. Rural credit seen has undergone a major transformation the quality of cultivator’s credit has improved. It has become more and more production oriented and institution biased correspondingly. Individuals have access to rural credit options under certain circumstances. For example, novice farmers and ranchers may be granted a loan or line of credit to manage the acquisition and upgraded of an existing farm operation or the establishment of a new one. Farmers and ranches are sometimes extended credit of this type when some sort of natural disaster has reined crops and threatens the ongoing operation of the branch or farm.

Classification and identification of agricultural products based on improved MobileNetV2.

With the advancement of technology, the demand for increased production efficiency has gradually risen, leading to the emergence of new trends in agricultural automation and intelligence. Precision classification models play a crucial role in helping farmers accurately identify, classify, and process various agricultural products, thereby enhancing production efficiency and maximizing the economic value of agricultural products. The current MobileNetV2 network model is capable of performing the aforementioned tasks. However, it tends to exhibit recognition biases when identifying different subcategories within agricultural product varieties. To address this challenge, this paper introduces an improved MobileNetV2 convolutional neural network model. Firstly, inspired by the Inception module in GoogLeNet, we combine the improved Inception module with the original residual module, innovatively proposing a new Res-Inception module. Additionally, to further enhance the model's accuracy in detection tasks, we introduce an efficient multi-scale cross-space learning module (EMA) and embed it into the backbone structure of the network. Experimental results on the Fruit-360 dataset demonstrate that the improved MobileNetV2 outperforms the original MobileNetV2 in agricultural product classification tasks, with an accuracy increase of 1.86%.

Soil Moisture Conservation through Crop Diversification and Related Ecosystem Services in a Blown-Sand Area with High Drought Hazard.

Soil moisture reserves are a key factor in maintaining soil fertility and all other related ecosystem services (including carbon sequestration, soil biodiversity, and soil erosion control). In semiarid blown-sand areas under aridification, water preservation is a particularly crucial task for agriculture. The international Diverfarming project (2017-2022), within the EU Horizon 2020 Program, focused on the impacts of crop diversification and low-input practices in all pedoclimatic regions of Europe. In this three-year experiment conducted in the Pannonian region, the impact of intercropping asparagus with different herbs on some provisioning and regulating ecosystem services was evaluated in the Kiskunság sand regions. Relying on findings based on a range of measured physical and chemical soil parameters and on crop yields and qualitative properties, advice was formulated for farmers. The message drawn from the experiment is somewhat ambiguous. The local farmers agree that crop diversification improves soil quality, but deny that it would directly influence farm competitiveness, which primarily depends on cultivation costs (such as fertilization, plant protection, and labour). Further analyses are needed to prove the long-term benefits of diversification through enriching soil microbial life and through the possible reduction of fertilizer use, while water demand is kept at a low level and the same crop-quality is ensured.

Advancements in Artificial Intelligence (AI) for enhanced insights and automation in rice agriculture: A systematic review

With the rising global demand for rice, improving production efficiency through advanced technologies like artificial intelligence (AI) is crucial. This systematic review gathered recent literatures on learning algorithm models applied to automate rice agriculture tasks. The objectives were to analyze the performance accuracy of different machine learning algorithms for rice classification and determine the most effective models. The 116 studies from 2016-2023 were screened and 70 were included. The algorithms were evaluated by weighted mean accuracy percentage across studies while maintaining consideration to sample sizes. The results showed the DenseNet121 deep convolutional neural network achieved the overall highest accuracy of 99.98%, also topping rice disease detection. For variety classification, Deep Neural Networks reached 99.95% accuracy by learning complex visual differences. Adaptive Neuro-Fuzzy Inference System led in grading quality of 98.6% by discerning grain features. Larger datasets improved accuracy indicating that the more training data has, it enhances model accuracy. The review demonstrates AI’s significant potential to automate essential aspects of rice production. Further research expanding standardized algorithm evaluations is recommended to strengthen the evidence-base and support integration of AI for intelligent, sustainable rice agriculture.

Evaluating Agriculture Robots: Case Study and Analysis

The technical revolution paved the way for the development of robots that can now operate in a variety of areas. The smart farming technique called robotic agrifarming has the potential to alleviate the worldwide labor shortage while increasing agricultural productivity and food safety. From planting to harvesting, agribots perform field operations that enhance soil quality and ensure long-term growth. That's why it's essential to create a decision-support system that helps choose the best robot for an agricultural task. The goal of this study is to create an integrated decision system for assessing agriculture robots using the method based on removal effects of criteria (MEREC). Founded on a lot of alternatives like project viability, ecosystem aptness, community conditions, governmental embracing, and profit, the performance of seven field robots is evaluated. The advantage of the developed ranking technique is that it follows the additive ratio assessment (ARAS) method, which is indeed a specific technique used for multi-attribute decision-making (MADM), particularly in the field of environmental management and evaluation. It's commonly employed when there are multiple factors or criteria to consider in decision-making processes and prioritizing the alternatives.