## 3 Sensing Per Crop
### Sensing Apple Diseases
#### 3.2.1 Sensors and Algorithms  
Post-harvest detection of defects are carried out in automatic sorting lines (Table 2). Different researchers [56, 71, 155-157] analyzed apple diseases mainly for fruit quality sorting and detection of the three diseases at the same time. Differences in spectral responses at 450, 675 and 686 nm with a 6 nm bandwidth provided excellent apple scab detection independent of apple cultivar and color [56]. This study showed that the asymmetric second difference method and principal component analysis (PCA) gave very similar results for the disease detection, fungal contamination, bruises and soil contamination on apples (Table 4). A neural network using up to 200 neurons was used to detect diseases on apples using entropy, local homogeneity, energy and inertia as image parameters and achieved an 89% success rate on the detection of damaged apples [158]. An automatic near infrared vision system was developed for apple defect inspection using a monochrome CCD camera attached with a 700 nm long-pass filter. The inspection procedure consisted of four steps: blob extraction, feature extraction, rule base construction and recognition [58]. A near-infrared (NIR) and a mid-infrared (MIR) dual-camera vision system was also used for quality inspection on refrigerated apples [159]. The MIR cameras had a sensitive spectrum range from 7.5 to 13.5 um and detected only stem-ends and calyxes (Table 3). Recognition rates on refrigerated Red Delicious apples of about 94% for stem-ends and 92% for calyxes were achieved. The correct classification rates of good and defective apples were, 100% and 92%, respectively.  
A line-scan X-ray imaging device detected bruises in Red Delicious and Golden Delicious apples [59]. One day and one month old bruises were analyzed, testing spatial and transform features. Best classification results were obtained using an artificial neural network and two kinds of features: spatial edge features and discrete cosine transform coefficients (Table 2). For old bruises, accuracies of 90% and 93% were achieved, respectively, for Red Delicious and Golden Delicious apples. New bruises were not adequately separated using this methodology (accuracy was approximately 60% for both apple varieties).  
Remote sensing of scab infection plots throughout the vegetation period applies a standardized difference vegetation index (SDVI) to it. A combination of visible bands gives a much lower detection level than combinations of wavebands in the NIR. Symptoms of the infection leaves at the beginning did hardly show any visual infection (brown spots) but apparently the fungus was already present in the leaves affecting its internal structure [92, 160, 161]. Three months later the disease in the visible part of the spectrum was clear, as the damage to the internal structure of the leaves became larger. Out of these results it was concluded that a common vegetation index such as the normalized difference vegetation index (NDVI) is not the ideal one for early scab detection; combinations of NIR bands will perform much better (Table 4). Early stress detection for scab, making use of SDVI's can best been done using as second wavelength a waveband situated between 750 nm and 1,400 nm and first wavelength between 750 nm and 850 nm [162].  
More recently in Germany, the apple scab model ASCHORF was developed and can provide practical recommendations to plant protection services and apple growers [163]. The modeled infection risk is dependant on temperature and leaf wetness duration. Leaf wetness duration is calculated but not measured and is based on energy balance principles. The model uses a sliding 10-day time series and acquires data for the previous four days from the standard meteorological network and then inputting grid point data from numerical weather prediction models.