Development of a smart machine-vision-based system to detect water stress in greenhouse tomato plants

Timely detection of water stress in agricultural crops is important. In this paper, a smart classification algorithm was developed to detect water stress in tomato plants that were grown in the greenhouse. During the growth period, thermal and visible light images were acquired from the canopy tops in two states: (1) plants in normal conditions; and (2) plants under water stress. Images were obtained using a camera that recorded simultaneous frames of thermal and visible (red, green, and blue (RGB)) features. Based on these features, 22 parameters were defined and applied to classify the image frames. In order to develop an efficient algorithm, principal component analysis (PCA) was applied to optimize the classifying of parameters. For normalizing the data in PCA, 6 normalization methods were applied and assessed. Among them, peak normalization was the best as its PC1 and PC2 described 94% and 5% of total variation, respectively. Based on the PCA results, 9 parameters were found with most loadings as the most effective indexes that all obtained from the visible features. In other words, the thermal features were not as useful for detecting plant water stress. These parameters were used in multilayer perceptron neural networks (MLPNN) to develop the classification algorithm. The resulting mean-square error and r values for the MLPNN with ten hidden layer were 6.05×10-3 and 0.9905, respectively which shows the robustness of the classification algorithm. This algorithm accuracy was 83.3%

Modelling Rupture Force based on Physical Properties – a Case Study for Roma Tomato (Solanum lycopersicum) Fruits

Biophysical properties of agricultural materials are important in designing of processing machines. In this study, some physical properties of tomato (Solanum lycopersicum) fruits were determined and their mutual relationships were studied. Dimensions (major diameter, minor diameter, and length), mass, volume, fresh and dry matter weight, as well as rupture point under uniaxial loading were measured. Other properties; including Poisson's ratio, modulus of elasticity, energy for rupture, density, arithmetic mean diameter, geometric mean diameter, diameter of equivalent volume sphere, and sphericity were calculated accordingly. Statistical analysis of the data indicated significant correlations between the rupture force and fresh weight, volume, dry weight, major diameter, minor diameter, arithmetic mean diameter, geometric mean diameter, and diameter of equivalent volume sphere. Fruit volume has significant correlations with fresh weight and average diameter. Correlations between major and minor diameters are very significant in this variety. Finally, regression equations were developed to model tomato biophysical properties

Development of a smart machine vision based system to detect water stress in greenhouse tomato plants

Timely detection of water stress in agricultural crops is important. In this paper, a smart classification algorithm was developed to detect water stress in tomato plants that were grown in the greenhouse. During the growth period, thermal and visible light images were acquired from the canopy tops in two states: (1) plants in normal conditions; and (2) plants under water stress. Images were obtained using a camera that recorded simultaneous frames of thermal and visible (red, green, and blue (RGB)) features. Based on these features, 22 parameters were defined and applied to classify the image frames. In order to develop an efficient algorithm, principal component analysis (PCA) was applied to optimize the classifying of parameters. For normalizing the data in PCA, 6 normalization methods were applied and assessed. Among them, peak normalization was the best as its PC1 and PC2 described 94% and 5% of total variation, respectively. Based on the PCA results, 9 parameters were found with most loadings as the most effective indexes that all obtained from the visible features. In other words, the thermal features were not as useful for detecting plant water stress. These parameters were used in multilayer perceptron neural networks (MLPNN) to develop the classification algorithm. The resulting mean-square error and r values for the MLPNN with ten hidden layer were 6.05×10-3 and 0.9905, respectively which shows the robustness of the classification algorithm. This algorithm accuracy was 83.3%