Abstract
Artificial intelligence (AI) shows great promise for transforming the agriculture sector and can enable the development of many modern farming practices over conventional methods. Nowadays, AI agents and agentic AI have attained popularity due to their autonomous structure and working mechanism. This research work proposes an agentic AI framework that integrates multiple agents developed for farming land to promote climate-smart agriculture and support United Nations (UN) sustainable development goals (SDGs). The developed structure has four agents: Agent A for monitoring soil properties, Agent B for weather sensing, Agent C for disease detection vision sensing in rice crops, and Agent D, a multi-agent supervisor agent chatbot connected with the other agents. The overall objective was to connect all agents on a single platform to obtain sensor data and perform a predictive analysis. This will help farmers and landowners obtain information about weather conditions, soil properties, and vision-based disease detection so that appropriate measures can be taken on agricultural land for rice crops. For soil properties (nitrogen, phosphorus, and potassium) from Agent A and climate data (temperature and humidity) from Agent B, we deployed the long short-term memory (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1D-CNN) predictive models, which achieved an accuracy of 93.4%, 94%, and 96% for Agent A; a 0.27 mean absolute error (MAE) for temperature; and a 2.9 MAE for humidity on the Agent B data. For Agent C, we used vision transformer (ViT), MobileViT, and RiceNet (with a diffusion model layer as a feature extractor) models to detect disease. The models achieved accuracies of 95%, 98.5%, and 85.4% during training respectively. Overall, the proposed framework demonstrates how agentic AI can be used to transform conventional farming practices into a digital process, thereby supporting smart agriculture.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 8
Claim · model-written summary
Agent A LSTM reached 93.4% accuracy for soil property predictions.
Not reportedTaxon
Verbatim
e0“For soil properties (nitrogen, phosphorus, and potassium) from Agent A and climate data (temperature and humidity) from Agent B, we deployed the long short-term memory (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1D-CNN) predictive models, which achieved an accuracy of 93.4%, 94%, and 96% for Agent A”
t0“Agentic AI Framework to Automate Traditional Farming for Smart Agriculture”
Claim · model-written summary
Agent A GRU reached 94% accuracy for soil property predictions.
Not reportedTaxon
Verbatim
e0“gated recurrent unit (GRU), and one-dimensional convolutional neural network (1D-CNN) predictive models, which achieved an accuracy of 93.4%, 94%, and 96% for Agent A”
t0“Agentic AI Framework to Automate Traditional Farming for Smart Agriculture”
Claim · model-written summary
Agent A 1D-CNN reached 96% accuracy for soil property predictions.
Not reportedTaxon
Verbatim
e0“one-dimensional convolutional neural network (1D-CNN) predictive models, which achieved an accuracy of 93.4%, 94%, and 96% for Agent A”
t0“Agentic AI Framework to Automate Traditional Farming for Smart Agriculture”
Claim · model-written summary
Agent B temperature prediction had MAE 0.27.
Not reportedTaxon
Verbatim
e0“a 0.27 mean absolute error (MAE) for temperature”
t0“Agentic AI Framework to Automate Traditional Farming for Smart Agriculture”
Claim · model-written summary
Agent B humidity prediction had MAE 2.9.
Not reportedTaxon
Verbatim
e0“a 2.9 MAE for humidity on the Agent B data”
t0“Agentic AI Framework to Automate Traditional Farming for Smart Agriculture”
Claim · model-written summary
ViT rice disease detection reached 95% training accuracy.
- Taxon
- rice crops
Oryza sativae1 - Outcome
- accuraciese0
- Value
- 95%e0
- Qualifier
- during traininge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“For Agent C, we used vision transformer (ViT), MobileViT, and RiceNet (with a diffusion model layer as a feature extractor) models to detect disease. The models achieved accuracies of 95%, 98.5%, and 85.4% during training respectively”
t0“Agentic AI Framework to Automate Traditional Farming for Smart Agriculture”
e1“Agent C for disease detection vision sensing in rice crops”
Claim · model-written summary
MobileViT rice disease detection reached 98.5% training accuracy.
- Taxon
- rice cropsOryza sativae1
- Outcome
- accuraciese0
- Value
- 98.5%e0
- Qualifier
- during traininge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“For Agent C, we used vision transformer (ViT), MobileViT, and RiceNet (with a diffusion model layer as a feature extractor) models to detect disease. The models achieved accuracies of 95%, 98.5%, and 85.4% during training respectively”
t0“Agentic AI Framework to Automate Traditional Farming for Smart Agriculture”
e1“Agent C for disease detection vision sensing in rice crops”
Claim · model-written summary
RiceNet rice disease detection reached 85.4% training accuracy.
- Taxon
- rice cropsOryza sativae1
- Outcome
- accuraciese0
- Value
- 85.4%e0
- Qualifier
- during traininge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“For Agent C, we used vision transformer (ViT), MobileViT, and RiceNet (with a diffusion model layer as a feature extractor) models to detect disease. The models achieved accuracies of 95%, 98.5%, and 85.4% during training respectively”
t0“Agentic AI Framework to Automate Traditional Farming for Smart Agriculture”
e1“Agent C for disease detection vision sensing in rice crops”