Insect Killing Robot for Agricultural Purposes

Obasekore, Hammed; Fanni, Mohammad; Ahmed, Sabah M.

doi:10.1109/aim.2019.8868507

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The proposed technique is formulated to be used on agricultural robots. Therefore, the robot kinematics, dynamic model, and controller proposed in [ 68 ] are adopted to validate its usability. The robot is a mobile manipulator having a four-DOF RRRR manipulator with a skid-steering mobile base.…”

Section: Resultsmentioning

confidence: 99%

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda)

Obasekore

Fanni

Ahmed

et al. 2023

Sensors

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Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf stereo camera sensor data using deep learning holds several benefits for farmers, from simple robot configuration to early neutralization of this less agile but more disastrous stage. Machine vision technology has advanced from bulk spraying to precise dosage to directly rubbing on the infected crops. However, these solutions primarily focus on adult pests and post-infestation stages. This study suggested using a front-pointing red-green-blue (RGB) stereo camera mounted on a robot to identify pest larvae using deep learning. The camera feeds data into our deep-learning algorithms experimented on eight ImageNet pre-trained models. The combination of the insect classifier and the detector replicates the peripheral and foveal line-of-sight vision on our custom pest larvae dataset, respectively. This enables a trade-off between the robot’s smooth operation and localization precision in the pest captured, as it first appeared in the farsighted section. Consequently, the nearsighted part utilizes our faster region-based convolutional neural network-based pest detector to localize precisely. Simulating the employed robot dynamics using CoppeliaSim and MATLAB/SIMULINK with the deep-learning toolbox demonstrated the excellent feasibility of the proposed system. Our deep-learning classifier and detector exhibited 99% and 0.84 accuracy and a mean average precision, respectively.

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Section: Resultsmentioning

confidence: 99%

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda)

Obasekore

Fanni

Ahmed

et al. 2023

Sensors

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“…This integration has given birth to innovative solutions for pest detection, classification, and localization leading to a significant improvement in agricultural efficiency. Deep learning models have been extensively applied for the detection and diagnosis of plant diseases and pests offering promising results and large potential in image processing and data analysis (Ferentinos, 2018;Kamilaris and Prenafeta-Boldu, 2018;, These models have been particularly effective in early pest detection, such as the recognition of insect pests at the larval stage before planting, allowing for precise localization and targeted intervention to minimize chemical usage (Obasekore et al, 2023). This early identification allows for precise localization and targeted intervention minimizing the need for chemical pesticides.…”

Section: Advanced Imaging and Deep Learning For Pest Management And C...mentioning

confidence: 99%

Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation—a review

Ahmed,

Zhang,

Deng

et al. 2024

Front. Plant Sci.

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Vegetable cultivation stands as a pivotal element in the agricultural transformation illustrating a complex interplay between technological advancements, evolving environmental perspectives, and the growing global demand for food. This comprehensive review delves into the broad spectrum of developments in modern vegetable cultivation practices. Rooted in historical traditions, our exploration commences with conventional cultivation methods and traces the progression toward contemporary practices emphasizing the critical shifts that have refined techniques and outcomes. A significant focus is placed on the evolution of seed selection and quality assessment methods underlining the growing importance of seed treatments in enhancing both germination and plant growth. Transitioning from seeds to the soil, we investigate the transformative journey from traditional soil-based cultivation to the adoption of soilless cultures and the utilization of sustainable substrates like biochar and coir. The review also examines modern environmental controls highlighting the use of advanced greenhouse technologies and artificial intelligence in optimizing plant growth conditions. We underscore the increasing sophistication in water management strategies from advanced irrigation systems to intelligent moisture sensing. Additionally, this paper discusses the intricate aspects of precision fertilization, integrated pest management, and the expanding influence of plant growth regulators in vegetable cultivation. A special segment is dedicated to technological innovations, such as the integration of drones, robots, and state-of-the-art digital monitoring systems, in the cultivation process. While acknowledging these advancements, the review also realistically addresses the challenges and economic considerations involved in adopting cutting-edge technologies. In summary, this review not only provides a comprehensive guide to the current state of vegetable cultivation but also serves as a forward-looking reference emphasizing the critical role of continuous research and the anticipation of future developments in this field.

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“…Laser irradiation with a green light was also effective on mosquitoes, but it has never been tested on an autonomous robot [27]. Furthermore, weed processing is now actively commercialized [28]. Weed destruction is typically carried out by a targeted herbicide spray or mechanically based methods.…”

Section: Introductionmentioning

confidence: 99%

Pesticide-Free Robotic Control of Aphids as Crop Pests

et al. 2022

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Because our civilization has relied on pesticides to fight weeds, insects, and diseases since antiquity, the use of these chemicals has become natural and exclusive. Unfortunately, the use of pesticides has progressively had alarming effects on water quality, biodiversity, and human health. This paper proposes to improve farming practices by replacing pesticides with a laser-based robotic approach. This study focused on the neutralization of aphids, as they are among the most harmful pests for crops and complex to control. With the help of deep learning, we developed a mobile robot that spans crop rows, locates aphids, and neutralizes them with laser beams. We have built a prototype with the sole purpose of validating the localization-neutralization loop on a single seedling row. The experiments performed in our laboratory demonstrate the feasibility of detecting different lines of aphids (50% detected at 3 cm/s) and of neutralizing them (90% mortality) without impacting the growth of their host plants. The results are encouraging since aphids are one of the most challenging crop pests to eradicate. However, enhancements in detection and mainly in targeting are necessary to be useful in a real farming context. Moreover, robustness regarding field conditions should be evaluated.

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Insect Killing Robot for Agricultural Purposes

Cited by 5 publications

References 6 publications

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda)

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda)

Advancing horizons in vegetable cultivation: a journey from ageold practices to high-tech greenhouse cultivation—a review

Pesticide-Free Robotic Control of Aphids as Crop Pests

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