## 3 Sensing Per Crop
### Sensors and Algorithms  
It is a challenge to detect surface defects on tomatoes in an automatic sorting line. In comparison with bruises, the other surface defects, such as the blossom-end rot, sunscal, mold and cracks are not so difficult to recognize for a vision system. Tomato bruises were detected using a hyperspectral imaging setup [191] in the wavelength region between 400 and 1,000 nm (Table 2). Chemometrics tools were used to extract the effective wavebands for surface defects detection and for identifying the stem-end. _F. oxysporum_ was detected on tomatoes using spectral Fourier signatures with an accuracy of 91% [192, 193]. Spectral signatures were analyzed by a Fourier program providing different harmonics, which were discriminated to obtain differences between healthy and diseased plants. An automatic conveyor belt was developed to sample the inoculated tomatoes on-line using 1 nm spectral bandwidths acquired with a computerized spectrometer [194]. Four different concentrations were applied encountering 92% on detection accuracy for a concentration of 6.5 x 10\({}^{4}\) sporangiospores/mL. Reflectance at 670 and 960 nm [195] was used to detect mold and other surface defects. An electronic nose was used to evaluate the maturity [196] and to monitor the shelf life of tomatoes (Table 3). Results showed that pink-stage fruit could be distinguished from light-red stage and red-stage ones based on the E-nose using principal component analysis (PCA) and linear discriminant analysis (LDA). E-nose sensor response signals were predicted and the coefficients were 0.981 and 0.968 for initially light-red stage fruit and red-stage fruit, respectively (Table 4). A sensor wasdeveloped to sense _Rhizopus stolonifer_ by the peduncle scar. The tomato peduncle scar acts as a membrane to decrease water loss in harvested fruits. Air is sucked and the relative humidity measured. A controlled pressure was applied to green and red infected tomatoes obtaining 91% and 89% on detection accuracy, respectively. The advantage of this method is that monitoring is non-dependent on its maturity stage [197]. Disease-warning systems are the most effective ICM option for tomato disease control. These systems use research-based information about weather conditions that increase the risk of fungal disease outbreaks. Disease-warning systems allow growers to spray fungicides only when the risk of an outbreak (and accompanying potential economic loss) is sufficiently high. Daily weather data (maximum and minimum air temperature, hours of leaf wetness, maximum and minimum air temperature during the wet period, hours of relative humidity \(>\) 90% and daily rainfall) were the environmental inputs to FAST. Because epidemics of late blight (causal agent: _Phytopthhora infestans_) sometimes appeared in Pennsylvania tomato field, the BLITECAST model [198], a disease-warning model based on air temperature and hours of HR \(>\) 90%, was run in tandem with the FAST model. In Ontario, growers use another model called TOM-CAST, which provides disease-risk ratings as Daily Severity Values (DSV's) to apply fungicide spray only when the sum reaches a predetermined threshold. At each site, temperature, relative humidity, rainfall amount, and duration of wetness periods were recorded by electronic sensors at 5-minute intervals and summarized hourly by an automated datalogger. Data were downloaded to a PC via modems and telephone so that DSV's for TOM-CAST could be calculated for the period from noon to 11 a.m. each day. It saves from 2 to 3 sprays per season which corresponds to fungicide sprays up to 50%, saving 125 US/ha [199]. Percent incidence of anthracose was not significantly higher for thresholds of 20 or 25; with a DSV's threshold of 15, anthracose incidence was affected neither by sensor location nor by distance from the field to a weather station. Marketable yield for DSV's thresholds of 15 or 20, but not 25, was not significantly lower than for the weekly-spray treatment [200].