Pesticides: Impacts on Agriculture Productivity, Environment, and Management Strategies

Khan, Bilal Ahmad; Nadeem, Muhammad; Nawaz, Humaira; Amin, Muhammad; Abbasi, Ghulam Hassan; Ali, Muhammad Qasim; Ameen, Muhammad; Javaid, Muhammad Mansoor; Maqbool, Rizwan; Ikram, Muhammad; Ayub, Muhammad Ashar

doi:10.1007/978-3-031-22269-6_5

Cited by 40 publications

(18 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, gray mold, another major condition, generally damages 15–20% of strawberries and more than 50% in severe cases [ 7 ]. Pesticides used to prevent this are also used in trillions of US dollars, of which only 0.1% kills the target, and the rest is absorbed and distributed into the environment and causes pollution [ 8 , 9 ]. In order to minimize the economic and environmental damages caused by these pests, several attempts have been made to predict pests [ 1 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Lee,

Yun

2023

Plant Methods

View full text Add to dashboard Cite

Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO2 concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention.

show abstract

Section: Introductionmentioning

confidence: 99%

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Lee,

Yun

2023

Plant Methods

View full text Add to dashboard Cite

show abstract

“…The indiscriminate use of chemical insecticides also raises concerns surrounding its unintended consequences. Specifically, a decline in beneficial insect populations and the degradation of soil health have been attributed to the excessive deployment of these agents [ 12 ]. Furthermore, the contamination of water sources, which are crucial resources for both agriculture and human consumption, exacerbates the environmental toll.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Optimization of the Insecticidal Properties of γ-Al2O3 Nanoparticles Derived from Mentha pulegium By-Products to Xylosandrus crassiusculus (Carob Beetle)

Mohamed Abdoul-Latif,

Ainane,

Eddabbeh

et al. 2024

Molecules

View full text Add to dashboard Cite

This study concentrates on assessing the insecticidal attributes of the γ-Al2O3 nanoparticles derived from the remnants of Mentha pulegium, which include essential oil, ethanolic extract, and plant waste. The synthesis of the γ-Al2O3 nanoparticles was executed using a direct sol-gel procedure, affirming the crystal structure according to extensive physicochemical analyses such as UV-Vis, XRD, FTIR, and SEM. Evaluation of the insecticidal activity in vitro was conducted against Xylosandrus crassiusculus, a pest that infests carob wood, utilizing strains from diverse forests in the Khenifra region, situated in the Moroccan Middle Atlas. The lethal doses 50 ranged from 40 mg/g to 68 mg/g, indicating moderate effectiveness compared to the commercial insecticide Permethrin. Optimization of the conditions for the efficiency of the γ-Al2O3 nanoparticles was determined using experimental plans, revealing that time, humidity, and temperature were influential factors in the lethal dose 50 of these nanomaterials. Moreover, this study encompasses the establishment of correlations using Principal Component Analysis (PCA) and Ascending Hierarchical Classification (AHC) among various geographic, biological, and physical data, amalgamating geographic altitude and γ-Al2O3 nanoparticle insecticide parameters, as well as the attributes of the mechanical tests conducted on the carob wood affected by insects. The correlations highlight the close connections between the effectiveness of the insecticide, mountain altitude, and the mechanical parameters that were examined. Ultimately, these nanoparticles demonstrate promising potential as alternative insecticides, thus opening up encouraging prospects for safeguarding against carob wood pests.

show abstract

“…The substantial dependence on synthetic pesticides for managing arthropod pests, particularly within food supplies, presents notable environmental as well as human health hazards [ 1 , 2 ]. These hazards encompass mammalian toxicity, environmental pollution, and bioaccumulation.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Efficacy of Four Apiaceae Essential Oils against Nine Stored-Product Pests in Wheat Protection

Kavallieratos,

Eleftheriadou,

Boukouvala

et al. 2024

Plants

View full text Add to dashboard Cite

The Apiaceae family, known for aromatic plants producing bioactive essential oils (EOs), holds significance across sectors, including agrochemicals. This study evaluated the insecticidal potential of four Apiaceae EOs from Crithmum maritimum L., Trachyspermum ammi (L.) Sprague ex Turrill, Smyrnium olusatrum L., and Elwendia persica (Boiss.) Pimenov and Kljuykov against various significant storage pests (Sitophilus oryzae (L.), Trogoderma granarium Everts, Rhyzopertha dominica (F.), Tribolium castaneum (Herbst), T. confusum Jacquelin du Val, Oryzaephilus surinamensis (L.), Alphitobius diaperinus (Panzer), Acarus siro L., and Tenebrio molitor L.) on wheat. Insect mortality rates were monitored at intervals of 1, 2, 3, 4, 5, 6, and 7 days. Smyrnium olusatrum EO exhibited the highest efficacy, followed by T. ammi, C. maritimum, and E. persica EOs, although efficacy varied by species, developmental stage, and concentration. Notably, complete mortality occurred for several pests at 1000 ppm of S. olusatrum and T. ammi EOs. Gas chromatography–mass spectrometry (GC–MS) analysis revealed key compounds in these EOs, including myrcene, germacrone, and curzerene in S. olusatrum EO, and thymol, γ-terpinene, and p-cymene in T. ammi EO. These findings emphasize their potential as botanical insecticides. Smyrnium olusatrum and T. ammi EOs emerge as promising eco-friendly pest management options due to their efficacy, highlighted compound composition, and availability of biomass from both wild and cultivated sources.

show abstract

Pesticides: Impacts on Agriculture Productivity, Environment, and Management Strategies

Cited by 40 publications

References 84 publications

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Assessment and Optimization of the Insecticidal Properties of γ-Al2O3 Nanoparticles Derived from Mentha pulegium By-Products to Xylosandrus crassiusculus (Carob Beetle)

Exploring the Efficacy of Four Apiaceae Essential Oils against Nine Stored-Product Pests in Wheat Protection

Contact Info

Product

Resources

About