## 3 Sensing Per Crop
### Sensing Mango and Avocado Diseases
#### 3.1.1 Sensors and Algorithms  
A NIR model was developed to predict mango anthracose in fruits still hanging in the trees [136]. Discriminant analysis was used to determine the best wavelengths (690 nm, 710 nm and 515 nm) and predicted the disease when it was still not visible (Table 2). A better model used HSI together with the spectral bands to increase the prediction accuracies [137]. Black pulp is another disease that is not visible until the fruit ripens; and it was detected with a thin needle which pushed the pulp towards a photo detector which was able to detect the disease [138]. Mangoes could be sorted by firmness [139] as hard, soft and very soft with 90% accuracy at a speed of one fruit per second (Table 3). NIR reflectance spectra on the 760 -2500 nm spectral range were used to measure mango flesh dry matter [140]. The best correlating wavelengths found by discriminant analysis were 904, 872, 1660 and 1516 nm, providing a R\({}^{2}\) = 0.90 with flesh dry matter (Table 4). Hyperspectral imaging for estimating total soluble solids, water content and fruit firmness in mango was obtained [141]. The correlation coefficient for total soluble solids and water content prediction was 0.78 and 0.81, respectively, while firmness correlation coefficient was 0.88. \begin{table}
\begin{tabular}{l c c c c c} \hline  & Ultrasonic & Magnetic & Machine & Spectral & X-ray & Others \\  & & resonance & vision & analysis & \\ \hline Mango and avocado & & & & & \\ Potato & & & & & \\ Tomato & & & & & & Electronic nose \\ Apple & & & & & & \\ Citric & & & & & & \\ Grapes & & & & & Chlorophylll fluorescence \\ \hline \end{tabular}
\end{table} 
Table 2: Sensors used for post-harvest detection of diseases and defects. X-ray imaging was proven to be reliable when the mango is cut open [142, 143]. MRI images obtained of mango fruits infected by the fruit fly showed dark areas in the seed eaten by the weevil, meanwhile healthy fruits showed a uniform light grey area [49]. X-ray can be considered a better technique for weevil detection in mango than MRI due to its lower cost [144]. A useful relationship between dry matter and the ratio of the oil/water were obtained with NMR in intact avocado that had desirable features for high-speed sorting [145]. [PERSON] avocado harvested at four different times during a growing season was analyzed by both reflectance and NIR spectroscopy to determine fruit dry matter. The model gave a R\({}^{2}\) = 0.88, suggesting NIR avocado grading on the basis of their DM content, to improve taste and oil content. Three of these wavelengths were obtained in the vicinity of 900-920 nm [146]. Low-altitude aerial color infrared (CIR) imagery identified _Phytophthora cinnamomi_ infestation in avocado orchards [147]. Two anthracose predictive models based on temperature and moisture were developed in Australia and Phillipines. The Mango Anthracnose Estimator (MAE) uses as variables the temperature and wetness duration for the prediction of dark approssoria production from conidia applied to detached mango leaves [148]. The use of the model resulted in a reduction of four to eight \begin{table}
\begin{tabular}{l c c c} \hline  & PCA & LDA & Neural networks & Others \\ \hline Mango and avocado & & & \\ Potato & & & \\ Tomato & & & \\ Apple & & & \\ Citric & & & \\ Grapes & & & \\ \hline \end{tabular}
\begin{tabular}{l c c c} \hline  & Disease warning & \multicolumn{3}{c}{Remote sensing} \\ \cline{3-4}  & systems & Spectral ratios \& NDVI & Infrared & Fluor \& Thermog. \\ \hline Mango and avocado & & & \\ Potato & & & & \\ Tomato & & & & \\ Apple & & & & \\ Citric & & & & \\ Grapes & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Algorithms used for the different operations on postharvest. \begin{table}
\begin{tabular}{l c c c} \hline  & Maturity \& Bruise & TSS, firmness & Disease det. & Freezing \& chilling injury \\ \hline Mango and avocado & & & & \\ Potato & & & & \\ Tomato & & & & \\ Apple & & & & \\ Citric & & & & \\ Grapes & & & \\ \hline \end{tabular}
\end{table}