Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
End-to-End AIoT Based Solution to Improve Crop Yield

End-to-End AIoT Based Solution to Improve Crop Yield

Paperback

Technology & Engineering

ISBN10: 6630308867
ISBN13: 9786630308860
Publisher: LAP Lambert Academic Publishing
Published: Aug 9 2026
Pages: 120
Weight: 0.38
Height: 0.28 Width: 6.00 Depth: 9.00
Language: English
The fast growth of the agricultural sector requires innovative keys to address problems such as low productivity, resource inefficiency, and the need for timely decisionmaking support for farmers. Motivated by these challenges, this research introducestwo novel systems-AIoTST-CR and VotTomNet-to enhance crop recommendationand disease diagnosis, thereby contributing to sustainable and precision agriculture.The proposed AIoTST-CR system integrates soil testing hardware with AI models torecommend the most appropriate crops based on soil parameters. This approach significantly reduces input costs and fertilizer usage while improving crop yield. Performanceevaluation revealed that Bayes method and Random Forest attained the maximum levelof accuracy, making sure that crop suggestions are trustworthy. A dedicated mobile application, Soil2Crop, was also developed to provide farmers with an intuitive platformfor accessing actionable insights.In parallel, the VotTomNet framework addresses tomato leaf disease diagnosis byensembling six up-to-date pre-trained deep learning models ( InceptionNet, VGG16, DenseNet, MobileNet, ResNet, & EfficientNet).

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