This study analyses the effect of browning through image analysis based on colour and textural features in fresh-cut apple slices. A computer vision system (CVS) was developed for image acquisition, which consisted of a digital camera and a florescent lamp source for illumination with a contrasting background. The CVS was calibrated using standard colour values and a model was developed by artificial neural network technique. Three varieties of apples such as Honey crisp, Granny Smith, and Golden Delicious were used for the analysis. The apples were freshly cut and subjected to image acquisition. Normalized colour features (L*, browning index, hue, and colour change) and textural features (entropy, contrast, and homogeneity) were analysed from the acquired images. The varieties Honey Crisp and Granny Smith did undergo browning within 120 min, whereas Golden delicious did not brown significantly. The study concluded that colour and textural features were important decision features for detecting browning in apples through image analysis.

Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. Spectral analysis of UAS acquired spatiotemporal images can be used to develop a statistical model to predict yield based on different phenological stages. Identifying critical vegetation indices (VIs) and significant spectral information could lead to increased yield prediction accuracy. The objective of this study was to develop a yield prediction model at specific phenological stages using spectral data obtained from a corn field. The available spectral bands (red, blue, green, near infrared (NIR), and red-edge) were used to analyze 26 different VIs. The spectral information was collected from a cornfield at Mississippi State University using a MicaSense multispectral red-edge sensor, mounted on a UAS. In this research, a new empirical method used to reduce the effects of bare soil pixels in acquired images was introduced. The experimental design was a randomized complete block that consisted of 16 blocks with 12 rows of corn planted in each block. Four treatments of nitrogen (N) including 0, 90, 180, and 270 kg/ha were applied randomly. Random forest was utilized as a feature selection method to choose the best combination of variables for different stages. Multiple linear regression and gradient boosting decision trees were used to develop yield prediction models for each specific phenological stage by utilizing the most effective variables at each stage. At the V3 (3 leaves with visible leaf collar) and V4-5 (4-5 leaves with visible leaf collar) stages, the Optimized Soil Adjusted Vegetation Index (OSAVI) and Simplified Canopy Chlorophyll Content Index (SCCCI) were the single dominant variables in the yield predicting models, respectively. A combination of the Green Atmospherically Resistant Index (GARI), Normalized Difference Red-Edge (NDRE), and green Normalized Difference Vegetation Index (GNDVI) at V6-7, SCCCI, and Soil-Adjusted Vegetation Index (SAVI) at V10,11, and SCCCI, Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARI_green) at tasseling stage (VT) were the best indices for predicting grain yield of corn. The prediction models at V10 and VT had the greatest accuracy with a coefficient of determination of 0.90 and 0.93, respectively. Moreover, the SCCCI as a combined index seemed to be the most proper index for predicting yield at most of the phenological stages. As corn development progressed, the models predicted final grain yield more accurately.

Colour and moisture content are two most important attributes of the commercial food product. Estimation of moisture content is very important to know the storability of the food product. It also relates to the process of drying in a fruit or vegetable. Extra drying and shrinkage deteriorates the quality of food product. The goal of the experiment was to examine the changes in RGB values of an apple during drying at different temperatures. In this study, emphasis was given on how the colour changes when there is a significant change in the moisture content of the apple. Three randomly chosen varieties of apples were sliced to 8 mm thickness and dried in vacuum oven at 60°C, 70°C, and 80°C. The loss of moisture was recorded for every 30 min interval and corresponding digital images were taken to determine the change in RGB value. The images that were captured during the study was analysed in MATLAB image analysis computer software. The analysis of moisture content and average colour share with respect to time showed that average colour share value decreases with time at all three temperatures. More than 50 per cent of variation in moisture content was explained by average colour share. There is a significant linear relationship between moisture content and colour changes in RGB and can be used to predict the moisture content of apple during drying process.

Development of yield forecast model using multiple regression analysis and impact of climatic parameters on spring wheat