The purpose of this study was to evaluate the impact of different environmental factors such as temperature, solar radiation, and relative humidity on the quality of strawberries in terms of their shape, size, and sugar accumulation. The experiment was carried out in a small walk-in greenhouse in Matsusaka city, Japan. Harunoka strawberries (Fragaria × ananassa Duch.) were cultivated from September to May of the following year. Production was evaluated on 20 February 2021 (peak season) and 5 April 2021 (end season). To evaluate the influence of environmental factors on strawberry fruit quality, the weight, shape, and soluble sugar content were recorded and compared to each other. According to the environmental data, the average temperature between day and night at peak harvest was around 12 °C, which was suitable for high-quality strawberry cultivation. However, the average temperature difference between day and night was approximately 4 °C at the end of the season. In addition, there were no significant differences in solar radiation and relative humidity between both seasons. Increasing temperatures led to the decline in the soluble sugar content at the end season. Thus, it can be concluded that the temperature difference between day and night is a major factor affecting strawberry production. The assessment of the impact of environmental conditions on strawberry quality can be used as a guideline not only in temperate climates, but also in other climates, such as in tropical countries.

Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight of Tilapia fish in turbid water environments. Additionally, the proposed method avoids the issue of using high-cost stereo cameras and instead uses only a low-cost video camera to observe the underwater life through a single channel recording. An in-house curated Tilapia-image dataset and Tilapia-file dataset with various ages of Tilapia are used. The proposed method consists of a Tilapia detection step and Tilapia weight-estimation step. A Mask Recurrent-Convolutional Neural Network model is first trained for detecting and extracting the image dimensions (i.e., in terms of image pixels) of the fish. Secondly, is the Tilapia weight-estimation step, wherein the proposed method estimates the depth of the fish in the tanks and then converts the Tilapia’s extracted image dimensions from pixels to centimeters. Subsequently, the Tilapia’s weight is estimated by a trained model based on regression learning. Linear regression, random forest regression, and support vector regression have been developed to determine the best models for weight estimation. The achieved experimental results have demonstrated that the proposed method yields a Mean Absolute Error of 42.54 g, R2 of 0.70, and an average weight error of 30.30 (±23.09) grams in a turbid water environment, respectively, which show the practicality of the proposed framework.