Plant diseases affect the availability and safety of plants for human and animal consumption and threaten food safety, thus reducing food availability and access, as well as reducing crop yield and quality. There is a need for novel disease detection methods that can be used to reduce plant losses due to disease. Thus, this study aims to diagnose tomato leaf diseases by classifying healthy and unhealthy tomato leaf images using two pre-trained convolutional neural networks (CNNs): Inception V3 and Inception ResNet V2. The two models were trained using an open-source database (PlantVillage) and field-recorded images with a total of 5225 images. The models were investigated with dropout rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, and 50%. The most important results showed that the Inception V3 model with a 50% dropout rate and the Inception ResNet V2 model with a 15% dropout rate, as they gave the best performance with an accuracy of 99.22% and a loss of 0.03. The high-performance rate shows the possibility of utilizing CNNs models for diagnosing tomato diseases under field and laboratory conditions. It is also an approach that can be expanded to support an integrated system for diagnosing various plant diseases.

Maturity is one of the most important factors associated with assessing the quality of tomatoes. The aim of this study is to develop a new device to measure the degree of ripeness of tomato fruits based on chlorophyll fluorescence. The results of this method, chlorophyll fluorescence, were compared with those of the widely used colorimeter for this purpose. Botanical variety of tomatoes “Alkazar” was used with different stages of maturity: green, breakers, turning, pink, light red, and red. The results indicated that specific parameters of the slow induction of chlorophyll fluorescence, such as the maximum chlorophyll fluorescence (Fm) and the coefficient of specific photosynthetic activity (Rfd), can be used to classify tomato fruits according to their maturity stage, as efficiently as the hue angle parameter of the color measurements. A correlation coefficient between the hue angle and the slow induction of chlorophyll fluorescence parameters was 0.96 with Fm, and 0.97 with Rfd. Using the hue angle or Fm, the fruits of all six-maturity stages were accurately classified. In conclusion, the developed device method is a non-destructive, innovative, convenient, and less time-consuming than the color-based method.

Agricultural production can achieve sustainability by appropriately applying agricultural mechanization, especially in developing countries where smallholding farmers lack sufficient agricultural machinery for their farming operations. This paper aimed to study the extent to which small-, medium-, and large-scale farms in the Delta of Egypt use agricultural mechanization in their wheat crop farming operations. K-means clustering was used to aggregate and analyze the scenarios implemented by farmers for wheat cultivation so as to suggest guidelines for each cluster of farmers on how to mechanize their indoor wheat agricultural operations to maximize production. The study is divided into two parts: Firstly, data were collected regarding the percentage of small, medium, and large farms; the cultivated area of wheat crops in small-, medium-, and large-scale farms; and the size of tractors, as an indicator of the mechanization available in the governorates of Egypt’s Delta. Secondly, data were collected through a questionnaire survey of 2652 smallholding farmers, 328 medium-holding farmers, and 354 large-holding farmers from Egypt’s Delta governorates. Based on the surveyed data, 14, 14, and 12 scenarios (indexes) were established for small-, medium-, and large-scale farms, respectively, related to various agricultural operations involved in wheat crop production. These scenarios were analyzed based on the centroids using K-means clustering. The identified scenarios were divided into three clusters for the three levels of farms. The data obtained showed the need for smallholding farmers to implement mechanization, which could be achieved through renting services. These findings, if implemented, would have huge social and economic effects on farmers’ lives, in addition to increasing production, saving time and effort, and reducing dependence on labor.

Classification of tomato fruit and control of post-harvest maturation based on the stage of maturity at harvesting, are necessary to ensure the highest possible quality and marketability of the final ripe product. A method of sorting tomatoes to assess the degree of their maturation based on the control of their chlorophyll fluorescence induction is proposed. Tomatoes (Black baron) at five different stages of maturity were used. Variance analysis (ANOVA) was performed and the Duncan’s mean values were compared at a significance level of p ≤0.05. In addition, the correlation between the chlorophyll fluorescence induction parameters and the maturity of the same fruit was carried out using the statistical software SPSS 20.0. The general pattern of fluorescence induction analysis was revealed; as the fruit matures, the value of both maximum fluorescence induction (Fm) and the coefficient of specific photosynthetic activity of tomatoes (Kf) decreases. Both Fm and Kf exhibited a statistically significant difference (p ≤0.05) between stages of maturity. A very strong negative correlation between slow chlorophyll fluorescence induction parameters (Fm and Kf) and maturity were observed. Moreover, there is a strong positive correlation between Fm and Kf. In conclusion, the slow induction of chlorophyll fluorescence is a good indicator of the degree of maturity of tomato fruits and the proposed method had better reflected the actual ripening process of fruit per maturity stage.