## 3 Sensing Per Crop
### Sensors and Algorithms
The TOM-CAST system can be used to schedule fungicide sprays for controlling early blight where weather data is available. The first fungicide application after transplanting should occur when 25 DSV's have accumulated [200]. If 25 DSV's have not accumulated before mid-July, the first fungicide should be applied at that time. Subsequent applications should occur when 18-22 DSV's have accumulated since the previous application. Scheduling fungicides using TOM-CAST provide good control of Septoria leaf spot. Those leaves sampled for the presence of Septoria lesions on leaves and stems were inspected. The fungus prefers cool, wet springs for early disease spread. Spectral ratio analysis based on principle component analysis [201] and clustered analysis was used in remote sensing of diseases (Table 4). They observed that the sensitive spectral wavelengths and reflectance values enabled them to discriminate _Phytopthhora infestants_ infection on tomatoes. Multi spectrum imaging detected the diseases and insect pests of tomato [202]. The experimental results showed that near infrared band (0.7-1.3 \(\upmu\)m) is more significant than visible light band in monitoring the crop diseases and insect pests. The 0.75-0.93 \(\upmu\)m band is useful for inspecting the tomato late blight. The spectral band of 750-930 nm is, statistically, the best for the remote sensing of tomato disease, followed by that of 950-1,030 nm and 1,040-1,130 nm [203]. Five narrow ranges are especially important for development of approaches to identify the diseased plants from the healthy ones: two peaks centering respectively at 850 nm and 1,050 nm, and three valleys respectively at 625 nm, 1,500 nm and 2,100 nm. Leaf reflectance spectra of tomato leaves damaged by leaf miner were obtained [204]. Spectral reflectance decreases significantly with the increasing severity level of infestation at the short wavelengths of near infrared 800-1,100 nm but changes for individual bands of 1,450 and 1,900 nm where spectral reflectance increases with the increasing infestation severity. Digital infrared thermography has been shown to be a useful tool for the prediction of diseases. Infrared thermography was tested on the detection of cucumber downy mildew caused by _Pseudoperonospora cubensis_[205]. The maximum temperature difference (MTD) within a leaf or a canopy turned out to be suitable for the differentiation of infected and non-infected tissue under controlled conditions. Environmental conditions during thermographic measurement, in particular air temperature and humidity, as well as water content and age of the leaf influenced the temperature of its surface. In some studies, conditions that enhance transpiration rate facilitated the detection of changes in leaf temperature of infected leaves. However, as modified by environmental conditions, digital infrared thermography alone is not suitable for the quantification of diseases in the field. Measurements of stomatal aperture during the early stages of pathogenesis indicated that the decreased water content of infected tissue 2 d after inoculation coincided with a slight increase in stomatal opening. In darkness, the aperture of stomata which had an average area of 25.5 um\({}^{2}\)in non-inoculated leaves dramatically increased due to the development of downy mildew and reached 160% and 280%, respectively, 3 d and 6 d after inoculation [206]. Two to three days after inoculation, the heterogeneity of the transpiration rate within infected leaves was significantly higher than for healthy leaves. Digital infrared thermography proved to be a simple but reliable parameter for this heterogeneity and may be used for the discrimination of healthy leaves or canopies [207] and those with downy mildew (Table 4). At later stages leaf tissue became necrotic, associated with a transpiration approaching zero and a drastic increase of local temperature; the average leaf temperature may be largely unaffected.