Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the accuracy.The performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.

Research on Dithianon to Evaluate Suspects of Reduced Sensitivity of <i>Venturia inaequalis</i>with Different Methodologies in Italian Apple Areas

Dithianon is a multi-site fungicide and has never been object of suspects and reports of reduced sensitivity and activity. Italian IFP technicians had the suspect of reductions of activity by this fungicide on Venturia inaequalis. Methodologies, in vitro and in vivo were carried out to verify this suspect. Populations poorly controlled with suspects on dithianon and sensible ones were utilized. The tests in vitro permitted to evidence light and non-significant reductions of sensitivity of poorly controlled populations with respect to sensible ones. In vivo tests on seedlings were non-reliable for a general low activity of dithianon. On the contrary, the original in vivo methodology on grafted apple plants showed several reductions of activity, with moderate levels and a spot distribution in orchards. The cause was probably due to the increased treatments with dithianon caused by problems on other groups of fungicides and by a high infective pressure in some years. It is discussed if this reduction can be considered a resistance phenomenon or a temporary modification of the interactions of plant-fungus-fungicide.