End-to-End AIoT Based Solution to Improve Crop Yield
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- Englisch ausgewählt
Fr. 89.90
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
09.08.2026
Verlag
LAP LAMBERT Academic PublishingSeitenzahl
120
Maße (L/B/H)
22/15/0.8 cm
Gewicht
197 g
Sprache
Englisch
EAN
9786630308860
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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