Deep-domain adaptation approach based crop disease prediction using UAV photometry images

Abstract

Agriculture is the backbone of any community, as it provides the most necessity for human survival. Diseases on plants/crops in agriculture reduces the productivity, thus its presence and removal are mandatory for good yield. Some of the common symptoms of disease in plants disease are leaf rust, stem rust, powdery mildew, leaf spot, birds-eye spot on berries, damping off of seedlings, sclerotinia, and chlorosis. These diseases can be identified visually by observing the physical condition of a plant’s leaves.This paper proposes the domain adaptation based model to predict the diseases in the crop in their early ages using UAV imagery. This gives information related to the water system, soil variety, pests, and fungal infestations. Crops images, collected by the UAVs, have information in the range of optical, especially visual spectra. Different features from these images can be extracted, which gives information about the health of plants in a manner that cannot be overserved otherwise. Another important feature of UAV based monitoring is its techno-economical approach to monitor the growth consistently and regularly i.e. at each month, day, or hourly basis if needed. The availability of crop information at this frequency helps scientists and farmers to take timely conter-measure decisions and actions. The proposed model has been developed using EfficientNet. The proposed model trained and tested on PlantVillage dataset. This dataset includes over 87K RGB images of healthy and diseased crop leaves, divided into 38 distinct classes. To overcome with the class imbalance problem, image enhancement, augmentation & rotation has been adopted. The complete dataset is splitted into 80/20 ratio of training and testing sets while maintaining the directory structure. Afterwards, model is trained and fine-tuned with UAV Photometry images using domain adaptation transfer learning approach.This leads to risk minimization.The model utilizes the methodologies of domain-invariant spaces and feature augmentation. The performance of the model evaluated in terms of specificity, sensitivity, accuracy and precession resulted in satisfactory performance with accuracy leading up to the order of 90%. Further, the proposed models are of lightweight in nature, which expands applicability and flexibility of model.