# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
### Crop Health Sensors (NDVI cameras, chlorophyll meters)  
The use of crop health sensors facilitates effective early detection of stress factors, nutrient deficiencies, and diseases, enabling rapid intervention and improved yields. These sensors assess crop vitality through the analysis of indicators such as leaf chlorophyll content, vegetation indices, and canopy temperature, most often using techniques such as the Normalized Difference Vegetation Index (NDVI) [77; 78]. The authors of [79] focus on the application of Unmanned Aerial Vehicles (UAVs) and remote sensing techniques in South African PA to improve crop monitoring for smallholder farmers. The research uses multi-spectral UAV imagery and a random forest machine-learning algorithm to assess the chlorophyll content of maize at different growth stages. Using near-infrared and red-edge wavelength bands, as well as derived vegetation indices, the study successfully estimates chlorophyll content, providing crucial information on the health of maize during phenological stages. The random forest model was performed optimally, particularly during the early reproductive, late vegetative, and early vegetative growth stages. The results highlight the spatial heterogeneity of chlorophyll within the maize field, providing valuable information for decision-making and farm management. This research establishes the importance of integrating drone imagery and machine learning algorithms in PA to help small-scale farmers maintain crop health and productivity. In the same context, a study [80] explores the integration of NDVI in automated greenhouses to improve PA. While automated greenhouses traditionally focus on environmental and soil variables, direct vegetation data, crucial for assessing crop health, is often neglected. The research aims to evaluate the integration of two types of NDVI measurement instruments into a greenhouse ecosystem, focusing on the correlation between NDVI levels and plant health. The correlation coefficients for healthy and stressed plants indicate the sensors' reliability in measuring NDVI, with minor deviations noted. Despite slight offsets observed for lower NDVI measurements, the correlation remained consistent and even slightly improved for stressed plants, suggesting that these variations do not significantly affect the sensors' reliability. The study highlights the importance of taking into account operational differences between sensors, such as data transmission rates, and highlights the pedagogical value of interdisciplinary research in the context of automated greenhouse technology. The authors of [81] address the limited application of remote sensing methods to assess crop vigor and yields in sub-Saharan Africa, mainly due to the constraints associated with satellite imagery. Using UAVs as a viable alternative, the study presents the effectiveness of a vegetation index derived from UAV imagery for assessing maize crop vigor and yields at different growth stages. The study, based on a quadcopter equipped with consumer cameras, including one modified for near-infrared capture, reveals that drone-derived GNDVI (Green Normalized Difference Vegetation Index) serves as a superior indicator of crop vigor and a more accurate estimator of yields compared to traditional field methods such as SPAD readings. The results highlight the potential of drone-based remote sensing, in particular, GNDVI, as a reliable and timely predictor of crop performance and yield, even in the context of small, complex farms in SSA. The authors of [82] present a real-time computer vision system designed for variable-rate agrochemical spraying to overcome the challenges associated with traditional spraying methods, such as overdosing or underdosing. The system uses a Random Forest classifier for weed/crop detection and classification. Training of the classification model with a customized data set precedes its deployment in the field of practical testing.