Abstract
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.
Highlights
Agriculture farming is considered as the base for human living because it is the primary source of food and income for most of the countries in the world
Internet of Things (IoT)-based sensors were used to obtain the data related to farming, and it is stored in a cloud framework where information is processed and sent to farmers’ mobile devices by applying some data mining techniques [36]
750 data instances are used for training the model, and the remaining 250 are used for testing the the model
Summary
Agriculture farming is considered as the base for human living because it is the primary source of food and income for most of the countries in the world. Land suitability analysis is a mandatory prerequisite for crop cultivation, which helps to obtain maximum production. Sensors play a significant role in collecting information about various factors such as soil, water, climate, etc., for agriculture development. With the help of data gathered from different sensors, land suitability analysis could be done, which would help farmers identify the current status of their agriculture land and improve their crop production. Water sensors, and biosensors are few that have been shown to have a significant role in measuring nature These sensors contribute to the smart farming system, especially in the handling of appropriate irrigation systems to help farmers. The integration of IoT, along with machine learning models, are providing the farmer recommendation system with appropriate inputs [19]. This would ensure minimal loss to the farmers, and the different sections presented here elucidate the model further
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