Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability

Sensors

SajjadHussain Chauhdary¹  Celestine Iwendi²  Dhivya Elavarasan³  N Deepa³  DuraiRaj Vincent³  Kathiravan Srinivasan³ 

[1] College of Computer Science and Engineering, University of Jeddah, Jeddah 21577, Saudi Arabia;Department of Electronics, BCC of Central South University of Forestry and Technology, Changsha 410004, China;School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India;

关键词: smart agriculture;    multi-layer perceptron;    agricultural data;    IoT in agriculture;    land suitability using sensors;    sensor data in agriculture;   

DOI  :  10.3390/s19173667

来源: DOAJ

【 摘 要 】

The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.

【 授权许可】

Unknown