Scientific Paper

Intelligent Drone Design for Precision Cashew Farming

Rajagopal et al. · 2024-07-16

What it covers

AI-powered drones equipped with advanced imaging and edge computing significantly enhance precision agriculture in cashew farming by enabling early disease detection and optimized pesticide application, achieving up to 95% accuracy in anthracnose classification and 99% accuracy in identifying healthy leaves, thereby improving sustainability, productivity, and resource efficiency in developing regions.

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Rajagopal et al.. 2024-07-16. Intelligent Drone Design for Precision Cashew Farming. https://ieeexplore.ieee.org/document/10589729 (AI & Environment Resource Hub; record paper-152; collection snapshot 2026-10-04).

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Available source metadata

Author or creator
Rajagopal et al.
Publisher
IEEE Xplore
Publication date
2024-07-16
Date precision
day
Geographic scope
Unknown
Language
unknown
Peer review
unknown
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Not yet verified
Snapshot import
2026-09-06
Legacy domain
Agriculture & Food
Legacy subdomain
AI-Driven Crop Disease Management and Unmanned Aerial Vehicle (UAV) Applications in Agriculture
journal
IEEE Xplore

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Metadata unconfirmed
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Source sheet: Scientific Paper · Row 156 · Original ID: paper-152.

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A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-28. Metadata not yet corroborated. The import date is not the original date added.

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Related through shared environmental topics

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Shared topics: Agriculture and food

Scientific Paper · 2025-08-13

A Deep Learning Model With Machine Vision System for Recognizing Type of the Food During the Food Consumption

This study develops a machine vision and deep learning framework, optimized with EfficientNetB7 and the Lion optimizer, that accurately identifies consumed food products across 32 categories, achieving up to 100% classification accuracy and supporting sustainable food consumption goals.

Source: NatureReachable at last check · Metadata unconfirmed

Shared topics: Agriculture and food

Scientific Paper · 2023-01-13

A Methods Guideline for Deep Learning for Tabular Data in Agriculture with a Case Study to Forecast Cereal Yield

This article offers practical guidelines for applying deep learning to tabular agricultural data, using a cereal yield forecasting case study to illustrate best practices in model selection, data preprocessing, and cross-validation for new users tackling agronomic problems.

Source: ScienceDirectAutomated access restricted · Metadata unconfirmed

Shared topics: Agriculture and food

Shared topics: Agriculture and food

Organization · Other

Abeya

Abeya is providing chocolate companies with cacao sourced directly from cacao cooperatives transitioning to regenerative practices. We are scaling regenerative agriculture by supporting tropical farmers with new technology and better prices. For farmers, the planet, and the companies who produce the products we love.

Source: OrganizationExcluded from automated checks · Metadata unconfirmed