# State-of-the-Art Internet of Things in Protected Agriculture
## 4 IoT Applications in Protected Agriculture
### Animal Farming  
Livestock and aquatic product farming is an important part of protected agriculture and an area where IoT applications have achieved good results. To achieve good control effects in animal breeding, IoT should not only overcome harsh environmental factors, but also pay attention to the effects of animal behaviors [99; 100]. IoT have been applied in monitoring and management of environment, animal, feed and farming process [101; 102]. The livestock monitoring items include information such as body temperature, weight, behavior, exercise volume, food intake, disease information and environmental factors, which can help people understand animal's own physiological and nutritional status and adaptability to external environmental conditions. In aquaculture, management projects focus on water quality such as dissolved oxygen content, water temperature and pH value [103; 104] because water quality greatly affects the growth of aquatic animals. With animal growth and nutrient optimization model and intelligent IoT equipment, it is possible to realize automatic feeding and optimal control of feeding time and intake according to their growth cycle, individual quality, feeding cycle and eating situation [105; 106]. The Osborne Industrial Company has produced TEAM automated electronic sow feeding (ESF) stations including pregnancy stations and estrus detection stations. The ESF stations identify the sow through the RFID tags worn by each sow and deliver the corresponding quantity and type of feed based on information such as parity, lyrical condition and gestational age. The estrus detection stations can detect rutting sows in the sow population and their detection accuracy is 7% higher than the manual ones. [PERSON] et al. presented a distributed monitoring IoT system for water quality monitoring. Their model was able to help fisheries acquire water quality parameters such as pH and temperature data in real time to optimize pond resources and prevent unwanted conditions [107]. [PERSON] et al. proposed an automated recirculation tilapia farming system based on the fuzzy algorithm. The results of this work showed that it saved water by 97.42% and the water quality environmental parameters were controlled within the acceptable range of tilapia culture [108]. Many researches have focused on analysis of animal behavior, health care and diagnosis and warning of diseases based on IoT [109; 110; 111]. [PERSON] et al. proposed an intelligent livestock surveillance system. They attempted many machine learning algorithms to process raw data of healthy and ill cows and finally obtained good results used a wavelet-domain ensemble classifier with 80.8% sensitivity and 80% specificity [112]. Wens Group, the largest livestock breeding enterprise in China, took the lead in carrying out research on the animal husbandry based on IoT and built the corresponding system for monitoring livestock vital signs, behavior and breeding environment information [113]. [PERSON] et al. collected a variety of peneaus vannamei information such as real-time data on the culture environment, disease image data and expert disease diagnosis and treatment and finally, constructed a remote intelligent diagnosis model based on IoT [114]. [PERSON] et al. proposed a web-based combined nutritional model for precisely predicting the growth, feed amount and nitrogen phosphorus output of gibel carp [115]. The current online diagnostics and early warning of animal diseases is at an early stage due to the lack of corresponding sensors and models. With upgrading of hardware and artificial intelligence algorithms, future research should focus on the improvement of Big Data depth algorithm and the establishment of comprehensive models of animal behavior and disease. A few studies have made in-depth research in monitoring animal odor and hazardous gas produced during the breeding process such as ammonia gas and sulfur dioxide [116]. [PERSON] developed an electronic nose network system for monitoring and real-time analysis of odors from livestock farms.