Plant diseases are spread by a variety of pests, weeds, and pathogens and may have a devastating effect on agriculture, if not handled in a timely manner. Farmers face umpteen challenges from a proper water supply, untimely rain, storage facilities, and several plant diseases. Crops disease is the primary threat and it causes enormous loss to farmers in terms of production and finance. Identifying the disease from several hectares of agricultural land is a very difficult practice even with the presence of modern technology. Accurate and rapid illness prediction for early illness treatment to crops minimizes economical loss to the individual and further proves to be productive for healthy crops. Many studies use modern deep learning approaches to improve the accuracy and performance of object detection and identification systems. The suggested method notifies farmers of different agricultural illnesses, prompting them to take further essential precautions before the disease spreads to the whole agricultural field. The primary objective of this study is to detect the illnesses as soon as they begin to spread on the leaves of the plants. Super-Resolution Convolutional Neural Network (SRCNN) and Bicubic models are employed in the system to identify healthy and diseased leaves with an accuracy of 99.175 % and 99.156 % respectively.

The growth-differentiation balance hypothesis (GDBH) predicts changes in susceptibility of plants against herbivores with changing resource availability. In the presented study we tested the validity of the GDBH for trees infected with a root pathogen. For this purpose Fagus sylvatica seedlings grown under different atmospheric CO2- and soil nitrogen regimes were infected with the root pathogen Phytophthora citricola. High nitrogen supply increased total biomass of beech regardless of the CO2-treatment, whereas elevated CO2 enhanced biomass only in the high nitrogen treatment. The responses of beech under the different growing regimes to the Phytophthora root infection were not in line with the predictions of the GDBH. Enhanced susceptibility of beech against P. citricola was found in seedlings grown under elevated CO2 and low nitrogen supply. Fifteen months after inoculation these plants were characterized by enhanced water use efficiency, by altered root–shoot ratios, and by enhanced specific root tip densities. Susceptibility of Fagus sylvatica to the root pathogen Phytophthora citricola increased under elevated CO2

Atmospheric CO2 concentrations are predicted to double within the next century and alter climate regimes, yet the extent that these changes will affect plant diseases remains unclear. In this study conducted over five years, we assessed how elevated CO2 and interannual climatic variability affect Cercospora leaf spot diseases of two deciduous trees. Climatic data varied considerably between the five years and altered disease expression. Disease incidence and severity for both species were greater in years with above average rainfall. In years with above average temperatures, disease incidence for Liquidambar styraciflua was decreased significantly. When significant changes did occur, disease incidence and severity always increased under elevated CO2. Chlorophyll fluorescence imaging of leaves revealed that any visible increase in disease severity induced by elevated CO2 was mitigated by higher photosynthetic efficiency in the remaining undamaged leaf tissue and in a halo surrounding lesions. Climatic variation had a greater impact than elevated CO2 on Cercospora diseases, especially since leaf photosynthetic efficiency increased under elevated CO2.