Non-Destructive Detection and Visualization of Chlorophyll Content in Cherry Tomatoes Based on Hyperspectral Technology and Machine Learning

The cherry tomato has an important economic value and an increasingly broad market, and the chlorophyll content of cherry tomato leaves can directly reflect the plant&rsquo:s photosynthetic ability, thus indirectly reflecting its growth status. Therefore, this paper proposes a regression detection method for chlorophyll in cherry tomato leaves by combining machine learning and hyperspectral technology to realize non-destructive, fast, and more accurate detection. Firstly, Moving-Average (MA) preprocessing was chosen as the pretreatment method for this paper, and three regression models of principal component regression (PCR), random forest (RF), and partial least squares regression (PLSR) were established with leaf chlorophyll under different nitrogen concentrations. The CARS_PLSR algorithm has the highest prediction accuracy with accuracy, precision, RMSEC, and RMSEP of 0.8790, 0.9187, 2.9581, and 2.5578, respectively. The study examined the impact of various nitrogen concentrations on the chlorophyll content of cherry tomato leaves, and it was found that the high concentration of nitrogen inhibited the SPAD value of cherry tomato leaves more than that of the low concentration, and the optimal concentration of nitrogen fertilization for tomatoes was 300 mg&middot:L&minus:1. Finally, a regression model was established by using CARS-PLSR combined with the pseudo-color map technology, and a distribution map of chlorophyll content in different SPAD value gradients of cherry tomato leaves was obtained, which could visualize the distribution of chlorophyll and its distribution sites in the leaves and understand the growth status of cherry tomatoes. The distribution of chlorophyll content in different SPAD values of cherry tomato leaves was obtained by using the CARS-PLSR regression model combined with pseudo-color map technology, which can visualize the distribution of chlorophyll in the leaves and the parts of distribution and understand the growth condition of cherry tomatoes. Finally, the optimal model is applied in conjunction with a sprayer to automate fertilizer application.