# Horticulture 4.0: Adoption of Industry 4.0 Technologies in
## 3 Intervention of Industry 4.0 in Horticulture
### AI in Horticulture  
Fresh and wholesome food is essential for feeding the expanding world population, and greenhouses and indoor agricultural techniques play a crucial role in this. In the past two decades, hyperspectral imaging research has developed, and in the decades to come, its use in horticulture is expected to grow. There are still challenges to the applicability. The automated detection of pests and diseases in plants empowers the effective monitoring of scalable fruit-and-vegetable crops. The detection of pests and diseases at the right time enhances pest- and disease-management systems [54]. The previous study concluded that AI algorithms can be implemented in horticulture for distinct applications, including fruit detection, pest, disease detection, weed detection, plant-stress detection, and yield prediction through the spectroscopy-and-camera system [55]. A study was implemented to identify common pests and diseases in apple fruit using sparse coding. Computer-vision techniques can identify pest- and disease-damaged fruits and provide data to assist in the detection and treatment of diseases and pests in the early stages [56]. Soil-organic-carbon (SOC) monitoring is a crucial characteristic of soil quality because it directly determines soil fertility and enables sustainable soil-nutrient management [57]. To improve SOC prediction, artificial neural networks (ANN), cubist regression, support-vector machines (SVM), multiple linear regression (MLR), and random forests (RF) were applied to the data of soil-nutrient indicators, total catchment area, and the topographic-wetness index. Automatic detection of plant pests and diseases can aid in the monitoring of large farms and gardens. The application of AI in the drying process of fruits and vegetables has received a lot of attention because it can generate better-dried fruit-and-vegetable products when combined with an efficient physical field [58,59].An IoT-based tool can determine whether a climacter fruit has been artificially ripened or not. To determine whether  
Figure 2: IoT for horticulture. the fruit is ripened artificially or naturally, machine-learning algorithms are applied [60]. We discuss these techniques and discuss the significance of combining computer-vision techniques with an autonomous robotic system that uses the deep-learning concept of artificial intelligence [61]. In order to reduce food waste, one study used sensors and analyzed gases produced by certain food products to detect rotten food at an early stage and boost accuracy. To forecast how frequently food will degrade, the researchers used machine learning, the IoT, and sensors. To assess the freshness of food, this study used ML and IoT. The implementation of vision-based hardware in robots enables the realization of intelligent spraying, crop-yield prediction and price forecasting, predictive insights, and disease diagnosis (Figure 3). In addition, during the supply chain, the AI-based IoT system enables the indoor environment conditions to be adjusted on the basis of external climatic conditions and travel time to avoid the spoilage of fruit and vegetables. Table 2 illustrates the different studies that implement AI for horticulture crops for disease detection, quality assessment, and grading. The table provides the different algorithms that have been implemented for feature selection, feature extraction, classification, and regression for identifying defects, bruising quality assessment, and grading of the fruit. Different studies have used different feature-selection and feature-extraction techniques such as random frog, random forest, linear discriminant analysis-based fully convolutional networks, competitive adaptive reweighted sampling-successive projection algorithms, uninformative variable elimination, successive projection algorithm, gray histograms, and gray-level co-occurrence matrices.