# Horticulture 4.0: Adoption of Industry 4.0 Technologies in
## 3 Intervention of Industry 4.0 in Horticulture
### IoT Intervention in Horticulture  
The potential of pests and plant diseases is inseparably associated in particular with-specific weather characteristics. Previous research concluded that humidity and rainfall have a significant impact on pathogen spread and propagation [37]. Pests and diseases are more inclined to spread as a result of the wind. According to the information presented above, pests and diseases are prevalent in crops, and monitoring of the related parameters can be achieved with the IoT. An intelligent monitoring system was proposed on the basis of the IoT with a global packet for radio service (GPRS) and Zigbee communication protocol for pest warning, planting works, and production-quality checks of apples with the assistance of soil sensors, meteorological sensors, and cameras [38]. The feasibility, yield, and irrigation water-use efficiency of an IoT-based precision-irrigation system with LoRaWAN technology on fresh-market tomato production was analyzed [39]. ET, Watermark 200 SS-5 soil-metric potential sensors, MP40, and a decision-support system were used to design and test four irrigation-scheduling treatments. A study was conducted with the integration of a camera module, and the images collected were used to analyze the water-management system as well as detect plant disease within a green environment [40]. Researchers established a technology platform using the IoT for environmental data acquisition, disaster warning, transmission, remote control, and an information push in vegetable greenhouses in real-time and lessen the influence of climate catastrophes on vegetable development [41]. The data collected by an IoT board are expected to be utilized to train machine-learning models for the development of intelligent automated indoor microclimate horticulture crops [42]. A database contains the results of the sensor analysis and it is also linked with data from the Indonesian Weather Agency, which contains daily meteorological data from the cultivation location [43]. To ensure the proper operation of the greenhouse automation system, multiple measuring stations are needed in a modern greenhouse to identify the local climate parameters in various areas of a large-scale greenhouse [44]. The IoT paradigm is allowing the scientific community to establish integrated settings where data could be automatically transferred among many distinctive networks to provide consumers with specific relevant information [45]. The security of the foods supplied can be ensured by utilizing the provenance data that are kept throughout the supply chain of vegetables, including during planting, harvesting, government oversight, testing, transportation, customs clearance, warehousing, repackaging, and internal testing [46; 47]. Every kind of pest and disease is believed to be harmful to plants and to have a serious unfavorable effect on horticulture. To decrease the frequent use of pesticides and fungicides and to anticipate when pests will arise, the IoT system was created [48]. Soft-computing technologies are used to identify fruits by combining the three essential characteristics of an object--color, shape, and texture. This method reduces the dimensions of the feature vector. As a result, the combined and normalized image features produce better classification accuracy with fewer training data [49]. Real-time supply-chain monitoring can give stakeholders insight into perishable food to better manage to price and take appropriate action to uphold quality requirements [50]. Farmers confront a variety of challenges when growing vegetables, including issues with seeds, managing pests and diseases, commodity costs, and product marketing. Farmers can use the internet and the concept of mobile cloud computing to access information and engage in an interactive dialogue about vegetable production through mobile learning [51]. A framework for papaya grading based on the Artificial Bee Colony algorithm was proposed to classify papaya fruits from digital photographs [52; 53].