In order to study the smart management of dairy farms, this study combined Internet of Things (IoT) technology and dairy farm daily management to form an intelligent dairy farm sensor network and set up a smart dairy farm system (SDFS), which could provide timely guidance for dairy production. To illustrate the concept and benefits of the SDFS, two application scenarios were sampled: (1) Nutritional grouping (NG): grouping cows according to the nutritional requirements by considering parities, days in lactation, dry matter intake (DMI), metabolic protein (MP), net energy of lactation (NEL), etc. By supplying feed corresponding to nutritional needs, milk production, methane and carbon dioxide emissions were compared with those of the original farm grouping (OG), which was grouped according to lactation stage. (2) Mastitis risk prediction: using the dairy herd improvement (DHI) data of the previous 4 lactation months of the dairy cows, logistic regression analysis was applied to predict dairy cows at risk of mastitis in successive months in order to make suitable measurements in advance. The results showed that compared with OG, NG significantly increased milk production and reduced methane and carbon dioxide emissions of dairy cows (p < 0.05). The predictive value of the mastitis risk assessment model was 0.773, with an accuracy of 89.91%, a specificity of 70.2%, and a sensitivity of 76.3%. By applying the intelligent dairy farm sensor network and establishing an SDFS, through intelligent analysis, full use of dairy farm data would be made to achieve higher milk production of dairy cows, lower greenhouse gas emissions, and predict in advance the occurrence of mastitis of dairy cows.

Yield calculation is an important link in modern precision agriculture, which is an effective means to improve breeding efficiency, and adjust planting and marketing plans. With the continuous progress of artificial intelligence and sensing technology, yield calculation schemes based on image processing technology have many advantages such as high accuracy, low cost, and non-destructive calculation, and have been favored by a large number of researchers. This article reviews the research progress of crop yield calculation based on remote sensing images and visible light images, describes the technical characteristics and applicable objects of different schemes, and focuses on detailed explanations of data acquisition, independent variable screening, algorithm selection and optimization. Common issues are also discussed and summarized. Finally, solutions are proposed for the main problems that have arisen so far, and future research directions are predicted, with the aim of achieving more progress and wider popularization of yield calculation solutions based on image technology.