Study of the Influence of Abiotic and Biotic Stress Factors on Horticultural Plants

In changing environmental conditions, horticulture plants are affected by a vast range of abiotic and biotic stresses which directly and indirectly influence plant condition [...]

The assessment of the Paeonia L. cultivars’ decorative value in planting

The assessment of decorative value of 35 peony cultivars growing in the Botanical garden plantings (Ekaterinburg, Sverdlovsk region, the Central Urals) were presented. For assessment were used a 100-rating scale which included the following features: flower colour, flower size, flower shape, flower doubleness, peduncle strength, bush decorative value, flowering abundance, blooming duration, flower scent, plant distinction, plant condition. The obtained assessments were compared to the literature data to examine for compliance with the main flower characteristics of peony cultivar and to reveal distinguishing features for peony plants in Sverdlovsk region and the Central Urals. As a result, 23 peony high-opportunity cultivars and 12 appreciable cultivars were distinguished and recommended for landscape gardening in Sverdlovsk region. For high-opportunity peony cultivars introduced in the Central Urals a descriptive characteristic based on the main flower decorative features was compiled.

Design of an IoT-Based Quantity Controlled Pesticide Sprayer Using Plant Identification

This chapter discusses the design of a quantitative controlled pesticide sprayer and the development of an efficient algorithm for plant identification. The whole system is controlled using the raspberry pi and convolutional neural networks (CNN) algorithm for training the proposed model. Once the algorithm identifies the plant by processing the image, it is captured by using a pi camera, and it determines the pesticide and its dosage. The sensors will collect the information related to the plant condition such as humidity and surrounding temperature, which is simultaneously sent to the farmers/agriculture officers through the internet of things (IoT), for the purpose of live analysis, and they are stored using cloud services, making the system suitable for remote farming. The proposed algorithm is trained mainly for three types of plant leaves, which include tomato, brinjal, and chilly. The CNN algorithm scores accuracy of 97.2% with sensitivity and specificity of 0.94 and 0.95, respectively. The robot is intended to encourage the agriculturists for next-level farming to facilitate their work.

Features Extraction of Capsicum Frutescens (C.F) NDVI Values using Image Processing

There is yet an application for monitoring plant condition using the Normalized Difference Vegetation Index (NDVI) method for Capsicum Frutescens (C.F) or chili. This study was carried out in three phases, where the first and second phases are to create NDVI images and recognize and extract features from NDVI images. The last stage is to assess the efficiency of Neural Network (N.N.), Naïve Bayes (N.B.), and Logistic Regression (L.R.) models on the classification of chili plant health. The images of the chili plant will be captured using two types of cameras, which can be differentiated by whether or not they have an infrared filter. The images were collected to create datasets, and the NDVI images' features were extracted. The 120 NDVI images of the chili plant were divided into training and test datasets, with 70.0% training and 30.0% test. The extracted data was used to test the classification accuracy of classifiers on datasets. Finally, the N.N. model was found to have the highest classification accuracy, with 96.4 % on the training dataset and 88.9 % on the test dataset. The state of the chili plant can be predicted based on feature extraction from NDVI images by the end of the study.