Deep learning approaches and interventions for futuristic engineering in agriculture

Chakraborty, Subhasish; Chandel, Narendra Singh; Jat, Dilip; Tiwari, Mukesh; Rajwade, Yogesh Anand; Subeesh, A.

doi:10.1007/s00521-022-07744-x

Cited by 24 publications

(5 citation statements)

References 247 publications

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“…To facilitate high frame-rate operations, the YOLOv5 object detection algorithm is used for tomato detection and classification, due to its superior performance and ease of use (Bai et al, 2023;Chakraborty et al, 2022;Kamilaris & Prenafeta-Boldú, 2018;Lawal, 2021;Saleem et al, 2021). included precision, recall, mAP@0.5, and mAP@0.5:0.95.…”

Section: Construction Of the Object Detectormentioning

confidence: 99%

Fruit growing direction recognition and nesting grasping strategies for tomato harvesting robots

Zheng,

Rong,

Zhang

et al. 2023

Journal of Field Robotics

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In recent years, the potential of robotic harvesting in greenhouse tomato production has garnered significant attention within the tomato industry. However, there is a lack of sufficient research on the complete replacement of manual harvesting with this technology. In this paper, we propose a tomato harvesting method that utilizes a nesting approach to simplify the process and minimize damage. The paper describes the tomato harvesting robot prototype, the visual system equipped with three vision‐based tomato detectors: YOLOv5_CBAM, which incorporates a convolutional block attention module; YOLOv5_SE, enhanced with a squeeze‐and‐excitation block; and a standard YOLOv5s model. Additionally, a novel shear gripping method for fruit bunches is presented, utilizing a bottom‐up snapping technique during harvesting. Point cloud data are utilized to determine the position of the tomato's main stem and bunch. The paper includes field tests and experimental findings, which indicate that the YOLOv5_CBAM model achieves the highest precision (82.62%) and recall (82.57%), outperforming YOLOv5_SE and standard YOLOv5s. Field experiments demonstrate that the improved end‐effector and vision system have significantly enhanced the robot's performance, achieving a 57.5% harvesting success rate in just 14.9 s.

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Section: Construction Of the Object Detectormentioning

confidence: 99%

Fruit growing direction recognition and nesting grasping strategies for tomato harvesting robots

Zheng,

Rong,

Zhang

et al. 2023

Journal of Field Robotics

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“…Remote sensing, within the precision agriculture approach, provides crucial information for monitoring natural resources, including crop growth, land use, soil moisture, plant health, and crop forecasting [9]. For widespread adoption in agriculture, it is essential to rely on measured data and integrate sources to ensure practical robustness [10]. Unmanned Aerial Vehicles (UAVs) equipped with multispectral (MS) sensors offer several benefits in precision agriculture, enabling the acquisition of high-resolution data that capture the spatial variability of attributes and crops [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

Andrade,

Montenegro,

Silva Neto

et al. 2024

AgriEngineering

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Precision agriculture requires accurate methods for classifying crops and soil cover in agricultural production areas. The study aims to evaluate three machine learning-based classifiers to identify intercropped forage cactus cultivation in irrigated areas using Unmanned Aerial Vehicles (UAV). It conducted a comparative analysis between multispectral and visible Red-Green-Blue (RGB) sampling, followed by the efficiency analysis of Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms. The classification targets included exposed soil, mulching soil cover, developed and undeveloped forage cactus, moringa, and gliricidia in the Brazilian semiarid. The results indicated that the KNN and RF algorithms outperformed other methods, showing no significant differences according to the kappa index for both Multispectral and RGB sample spaces. In contrast, the GMM showed lower performance, with kappa index values of 0.82 and 0.78, compared to RF 0.86 and 0.82, and KNN 0.86 and 0.82. The KNN and RF algorithms performed well, with individual accuracy rates above 85% for both sample spaces. Overall, the KNN algorithm demonstrated superiority for the RGB sample space, whereas the RF algorithm excelled for the multispectral sample space. Even with the better performance of multispectral images, machine learning algorithms applied to RGB samples produced promising results for crop classification.

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“…Polyhouse cultivation is an integral part of precision horticulture which envisages precise control of climate, water, fertilizer, and so on (Reddy, 2016), by technological interventions such as automatic climate control (Jat, Rajwade, et al, 2020), Internet-of-Things-based irrigation, wireless data transfer using ZigBee (Jonnala & Sathyanarayana, 2015), sitespecific input management (Chandel et al, 2016(Chandel et al, , 2021. The proven benefits of these technologies include offseason cultivation, higher input use efficiency, higher production and productivity, better quality of the agricultural produce; all these lead to an improved livelihood compared to open field cultivation (Bisbis et al, 2018;Chakraborty et al, 2022;Pachiyappan et al, 2022;Rao et al, 2022;Spehia, 2021).…”

Section: Introductionmentioning

confidence: 99%

Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

Jat

Dubey

Potdar

et al. 2023

Journal of Field Robotics

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Automated spraying practices are inevitable for modern polyhouse management to attain a broader objective of minimizing human exposure to agrochemicals. In the present study, an automated mobile robotic sprayer (AMRS) was developed to combat the increased human intervention and safeguard agricultural workers from potential health hazards. The system mainly comprises embedded sensors (ultrasonic, proximity, XBee) and controllers (Arduino, PLC). The controller drives the system on a piping track between the rows as well as on the head space achieving end-to-end automation for spraying operations. The system performance was evaluated on the tomato crop with respect to the physiological traits, yield and economics. Additionally, the study leveraged response surface methodology to optimize forward speed, spray distance, and working pressure of AMRS on the responses, droplet density, coverage, volume mean diameter (VMD) and application rate. Optimization of forward speed (0.79 km/h), spray distance (250 mm) and working pressure (0.40 MPa) resulted in 90.7 droplets/cm 2 droplet density, 47.1% coverage, 170.2 μm VMD and 86.0 mL/m 2 of application rate. Ergonomic aspects of AMRS were assessed by the parameters, human exposure, discomfort and postural assessment with respect to knapsack sprayer. The working heart rate of 103 beats/ min, work pulse of 12 beats/min, oxygen consumption rate of 916 mL/min and energy expenditure rate of 18.7 kJ/min recorded during the ergonomic evaluation of the AMRS were 25%, 75%, 42%, and 41% lower compared to manual spraying, respectively. Moreover, three-six times higher work pulse, cardiac cost, body part discomfort score and overall discomfort rating were observed which indicated more drudgery involved in manual spraying. State-of-the-art system developed for polyhouses would minimize the drudgery and health hazards, significantly. The system's resilience and effectiveness pave the way for its wider deployment where the use of agrochemicals are prevalent, particularly in small-size polyhouses.

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Deep learning approaches and interventions for futuristic engineering in agriculture

Cited by 24 publications

References 247 publications

Fruit growing direction recognition and nesting grasping strategies for tomato harvesting robots

Fruit growing direction recognition and nesting grasping strategies for tomato harvesting robots

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

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