Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.

Vegetation health assessment by using airborne multispectral images throughout crop production cycles, among other precision agriculture technologies, is an important tool for modern agriculture practices. However, to really take advantage of crop fields imagery, specialized analysis techniques are needed. In this paper we present a geographic object-based image analysis (GEOBIA) approach to examine a set of very high resolution (VHR) multispectral images obtained by the use of small unmanned aerial vehicles (UAVs), to evaluate plant health states and to generate cropland maps for Capsicum annuum L. The scheme described here integrates machine learning methods with semi-automated training and validation, which allowed us to develop an algorithmic sequence for the evaluation of plant health conditions at individual sowing point clusters over an entire parcel. The features selected at the classification stages are based on phenotypic traits of plants with different health levels. Determination of areas without data dependencies for the algorithms employed allowed us to execute some of the calculations as parallel processes. Comparison with the standard normalized difference vegetation index (NDVI) and biological analyses were also performed. The classification obtained showed a precision level of about $95\%$ in discerning between vegetation and non-vegetation objects, and clustering efficiency ranging from $79\%$ to $89\%$ for the evaluation of different vegetation health categories, which makes our approach suitable for being incorporated at C. annuum crop’s production systems, as well as to other similar crops. This methodology can be reproduced and adjusted as an on-the-go solution to get a georeferenced plant health estimation.

Recently, the use of small UAVs for monitoring agricultural land areas has been increasingly used by agricultural producers in order to improve crop yields. However, correctly interpreting the collected imagery data is still a challenging task. In this study, an automated pipeline for monitoring C. Annuum crops based on a deep learning model is implemented. The system is capable of performing inferences on the health status of individual plants, and to determine their locations and shapes in a georeferenced orthomosaic. Accuracy achieved on the classification task was 94.5. AP values among classes were in the range of $[63,100]$ for plant location boxes, and in $[40,80]$ for foliar area predictions. The methodology requires only RGB images, and so, it can be replicated for the monitoring of other types of crops by only employing consumer-grade UAVs. A comparison with random forest and large-scale mean shift segmentation methods which use predetermined features is presented. NDVI results obtained with multispectral equipment are also included.