A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment

This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops.

Research on Expert System of Crop Disasters Based on Neural Networks

In order to improve the efficiency of prediction and diagnose of crop diseases and pests, and solve the problems during the course of crop production and management, then an neural networks with weight adjustment of prediction model is proposed. The crop disasters are regarded as example, after the symptoms of disasters and features are classified, abstracted and coded, the adjustment of weight, optimization of network structure and reasonable adjustment of parameters of BP neural network are discussed, then a model is constructed to forecast the disasters of crop, the weight is used as knowledge to predict disasters of crop through studying training samples. Results have shown that the optimization expert system of crop disasters based on neural network has enhanced the ability of decision making of expert system, then greatly improved the accuracy and reliability of crop diagnosis.

Developing a Plant Clinic Database as an Educational and Predictive Tool for Master Gardener Programs

The North Carolina Cooperative Extension Service's New Hanover County Center provides the Plant Disease and Insect Clinic staffed by the Horticulture Extension Agent and Master Gardener volunteers. Residents bring in samples of weeds, diseases, and insects for identification and control recommendations. After the problem is diagnosed, a record of the information is used to construct a database that includes the date, phone number, crop, diagnosis, and control for each sample submitted. Between January 1993 and December 1999, Master Gardener volunteers entered more than 4,000 entries into a searchable/sortable electronic database to identify patterns of plant disorders. The database should be a useful tool for predicting local disease and insect cycles and aiding Master Gardeners in answering questions at the clinic and over the telephone. In addition, examination of historical records and entry of data into the database are excellent learning opportunities for new Master Gardeners.