New prospective approaches in controlling the insect infestation in stored grains

Mir, Shabir Ahmad; Mir, Mudasir Bashir; Shah, Manzoor Ahmad; Hamdani, Afshan Mumtaz; Sunooj, Kappat Valiyapeediyekkal; Phimolsiripol, Yuthana; Khaneghah, Amin Mousavi

doi:10.1016/j.aspen.2023.102058

Cited by 6 publications

(1 citation statement)

References 93 publications

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“…Therefore, the use of computer vision systems in grain inspection is becoming increasingly popular. These systems use advanced imaging techniques and ML algorithms to analyze images of grains and identify defects or impurities, such as broken kernels, foreign materials, or fungal infestations [46]. ANN, dense scale-invariant feature transform (DSIFT) algorithm, and support vector machines (SVM) are ML techniques that have been successfully applied in the agriculture sector for the classification and identification of grains and other agricultural products.…”

Section: Grain Qualitymentioning

confidence: 99%

Artificial Intelligence: Implications for the Agri-Food Sector

et al. 2023

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Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized.

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Section: Grain Qualitymentioning

confidence: 99%

Artificial Intelligence: Implications for the Agri-Food Sector

et al. 2023

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Autonomous Robotic Ultrathin Laser‐Scribed Microheater for Effective Insect Control

Poon,

Zhong,

et al. 2024

Advanced Intelligent Systems

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Insects such as ants and termites have always been a common household hazard, closely correlated to various health risks causing allergies or infections. Present‐day chemical insecticides pose carcinogenic threats to human health and the environment, according to the World Health Organisation. Herein, a novel autonomous robotic ultrathin laser‐scribed microheater is presented as a safe, efficient, and cost‐effective alternative for insect control. A microheater is fabricated using an array of laser‐scribed micro‐wires and an autonomous robotic module, allowing for rapid and uniform heat transfer in less than 3 s. The integrated robotic microheater successfully (100%) eradicates household ants at 60 °C within 30 s. Such results significantly advance insect control, reducing treatment time from hours to seconds while attenuating chemical insecticides’ critical safety and health risks. Ultimately, this innovative approach demonstrates the promise of autonomous robotic microheaters for broader agriculture, healthcare, and manufacturing applications, where insect control is crucial.

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HCBiL-DMN: an effective food infestation detection from stored food grains using deep learning model

Vedavathi,

Suhas Bharadwaj

2024

Multimed Tools Appl

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New prospective approaches in controlling the insect infestation in stored grains

Cited by 6 publications

References 93 publications

Artificial Intelligence: Implications for the Agri-Food Sector

Artificial Intelligence: Implications for the Agri-Food Sector

Autonomous Robotic Ultrathin Laser‐Scribed Microheater for Effective Insect Control

HCBiL-DMN: an effective food infestation detection from stored food grains using deep learning model

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