Effect of auto-exhaust pollution on leaf anatomy and micromorphology of Pistacia vera L

One of the important causes of air pollution is automobile emissions Automobile exhausts can be toxic and harmful to plants. In light of this, this study aimed to examine the anatomical and micromorphological effects of auto exhaust pollution on Pistacia vera L. (Pistachio), an important agricultural plant. Pistachio leaflet samples taken from heavy traffic areas and rural areas were compared anatomically and micromorphologically. Also, the data were statistically evaluated. As a result of the study, it was seen that there are anatomical and micromorphological differences between the plant in the rural area and the plant in the heavy traffic area in the city center where air pollution and the number of cars are high. In particular, significant differences were detected in the epidermis surface and cuticle structure, trichome cover, stomatal characteristics, as well as mesophyll width, and epidermis anticlinal outer wall width. As a result, the study revealed that the agriculturally important P. vera is affected by pollution anatomically and micromorphologically.

Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments

The early detection and prevention of plant diseases that are an important cause of famine and food insecurity worldwide are very important for increasing agricultural product productivity. Not only the early detection of the plant disease but also the determination of its type play a critical role in determining the appropriate treatment. The fact that visual inspection, which is frequently used in determining plant disease and types, is tiring and prone to human error, necessitated the development of algorithms that can automatically classify plant disease with high accuracy and low computational cost. In this study, a new hybrid plant leaf disease classification model with high accuracy and low computational complexity, consisting of the wrapper approach, including the flower pollination algorithm (FPA) and support vector machine (SVM), and a convolutional neural network (CNN) classifier, is developed with a wrapper-based feature selection approach using metaheuristic optimization techniques. The features of the image dataset consisting of apple, grape, and tomato plants have been extracted by a two-dimensional discrete wavelet transform (2D-DWT) using wavelet families such as biorthogonal, Coiflets, Daubechies, Fejer-Korovkin, and symlets. Features that keep classifier performance high for each family are selected by the wrapper approach, consisting of the population-based metaheuristics FPA and SVM. The performance of the proposed optimization algorithm is compared with the particle swarm optimization (PSO) algorithm. Afterwards, the classification performance is obtained by using the lowest number of features that can keep the classification performance high for the CNN classifier. The CNN classifier with a single layer of classification without a feature extraction layer is used to minimize the complexity of the model and to deal with the model hyperparameter problem. The obtained model is embedded in the NVIDIA Jetson Nano developer kit on the unmanned aerial vehicle (UAV), and real-time classification tests are performed on apple, grape, and tomato plants. The experimental results obtained show that the proposed model classifies the specified plant leaf diseases in real time with high accuracy. Moreover, it is concluded that the robust hybrid classification model, which is created by selecting the lowest number of features with the optimization algorithm with low computational complexity, can classify plant leaf diseases in real time with precision.