## 3 Sensing Per Crop
### Sensing Citrus Diseases
#### 3.3.1 Sensors and Algorithms  
A machine vision system [167] discriminated normal and diseased citrus leaf samples with greasy spot, melanose and scab (Table 2). The leaf sample discriminant analysis using Mahalanobis statistical classifier achieved over 95% accuracy for all classes when using hue and saturation texture features (Table 4). A back-propagation neural network algorithm achieved accuracies over 90% for all classes with the same texture features. Windscar was the most prevalent defect ranging from 23% on tangerine to 32.6% for grapefruit [168]. Classification was based on either Bayesian parametric techniques or on back propagation neural networks. Yielding the highest percent correct classification was the Bayesian approach. The parameters used for disease detection were HIS (Hue, Intensity and Saturation). RGB color systems using artificial neural network for orange-sorting are used in Brazil [169]. The feasibility of using machine vision system with neural networks to predict the sugar content or pH of orange fruit was tested [170]. The possibilities of adopting VIS/NIR spectra for measuring the quality characteristics of Satsuma mandarin was explored and developed a relationship between the VIS/NIR spectral measurements and major physiological properties like firmness [171], soluble solid content and acidity of the fruit (Table 3). The study concluded that the full spectral range of 400-2,350 nm has great potentials to assess the quality characteristics of mandarin fruit. An electronic nose was used to distinguish the maturity stage in mandarin [172]. Linear discriminant analysis was able to classify correctly 92% of the samples, while principal component analysis did not work properly. Spectral reflectance of citrus leaves changed as citrus caller lesions developed, being more pronounced in the 600-700 nm [173]. Typically absorption of pigments in the UV and in visible spectral range up to 500 nm is very high in the citrus peel, and low reflectance values are mainly originating from scattering in this range. From 720 nm up to the water absorption band centered at 980 nm, citrus peel has reflectance values around 70-80%, and decrease of the NIR reflectance due to softening (pre-necrosis stage) are obtained. Peel damage seen as tissue browning decreases the NIR reflectance. Changes in reflectance spectra can be seen already after 1-2 days after inoculation of orange fruits infested with _P. italicum_ and _P. citrophtora_[174]. MIR images of internal rots produced by _Alternaria citri, Diplodia natalensis_ and _Botrytis cinerea_ in oranges var _Naxel Barnfield_ detect fruits with healthy external appearance [46]. The black spot of citrus (_Citrus_ sp.) is caused by _Guignardia citricarpa_ with ascospore production depending on temperature, leaf wetness, and rainfall. Ascospore production was related to leaf wetness only in the orange orchard but was not related to total rainfall or temperature [175]. Temperature and relative humidity were not important factors in post-bloom fruit drop caused by _Colletotrichum gloeosporoides_. Leaf wetness 4-8 days before the target day was a significant factor and rainfall acts as a conidia disperser and as a provider of moisture for spore germination [176]. Citrus foot rot foliage loses their chlorophyll and become chlorotic (yellowish-white) in contrast to the dark-green foliage of healthy trees [177]. For quick assessments of vegetation stress in citrus crops in the visible and near infrared region of the electromagnetic spectrum, an inexpensive multi-band video system was used to distinguish between grapefruit and orange trees in the yellow-green band [178]. Aerial photography and videography have been found useful for tree inventory in the Merritt Island National Wildlife Refuge citrus groves [179]. Remote sensing evaluates plant stress monitoring salinity [180], delineation of saline soils [181], and the detection of insect infestations [182].