# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 4 Applications of Deep Learning in Agriculture
### Identification/Classification of Plant Disease  
Disease fungus, microbes, and bacteria obtain energy from the plants on which they dwell, affecting crop productivity. If not diagnosed in time, this can cause significant economic losses for farmers. Pesticides used to remove pathogens and restore crop functionality impose substantial financial pressures on farmers. Excessive pesticide usage also results in environmental degradation and affects agricultural areas' water and soil cycle [14]. Moreover, plant diseases impact the growth of particular species; hence, early detection of such stress is critical. Many deep learning models (DL) have been deployed to identify and classify plant diseases. Deep learning appears to possess considerable potential in terms of its increasing accuracy over time. Many new DL architectures and improvements/modifications to existing ones are proposed. Various modern visualization techniques are being used to identify and classify the symptoms of plant illnesses [15]. In [16], the author provided a method for identifying and classifying banana diseases based on the CNN model. It can assist the farmers in detecting the attacking illness in a timely, affordable, and swift manner. This system could detect two banana diseases, Sigatoka and speckle, by capturing a photo of an affected leaf and using a deep neural networks model. The authors of [17] used another deep learning model (AlexNet) to classify the type of disease in plants through the leaves pictures with high accuracy of results. A deep learning hybrid model is presented in [18] to classify the sunflower illnesses, including Alternaria leaf rot, Downy mildew, phoma rot, and vertilillium wilt. The author utilized the stacking ensemble learning technique to develop a hybrid model of H.VGG-16 and MobileNet. They built their dataset using google photos, and their proposed model provided an accuracy of 89.2%, which they claimed was better than other models. The research presented in [19] studied and compared the results of five different deep learning models, including H. Vgg16, Vgg19, ResNet50, ResNet50V2, and ResNet101V2, using both simulated data and images obtained from rice fields in the city of Gujranwala located in Pakistan. The ResNet50 model used an artificial dataset and was given accuracy of 75 %, while the ResNet101V2 model had an accuracy of 86.799 on the actual dataset. The author of [20] developed a machine learning-enabled mobile device for automating the diagnosis of plant leaf diseases. The developed system can classify 38 different types of illness using CNNs as the underlying deep learning engine. The researchers gathered an image dataset containing 96,206 photos of plant leaves from healthy and sick plants for the CNN model's training, validation, and testing. The user interface was created as an Android mobile application that farmers could use to photograph sick plant leaves. The disease category was then displayed, along with the 90 % confidence. This technique is meant to provide farmers with greater chances of keeping their crops healthy and avoiding the excessive use of fertilizers that are harmful to the crops. In paper [21], a transfer learning pre-trained deep neural network model was used for crop disease prediction to learn essential functions such as leaf characteristics directlyfrom the input data. They investigated various CNN topologies (ResNet, MobileNet, Wide ResNet, and DenseNet) and deep learning methodologies. The results revealed that the suggested method outperforms earlier methods in terms of memory and precision. Another CNN technique to detect, classify and identify plant disease is presented in [22]. It has proved output result accuracy ranging from 91 to 98 % and an average performance of 96.3% for thirteen different plant diseases. It can also differentiate between healthy and unhealthy leaves and distinguish them from their backgrounds. The author of [23] used a CNN model in agriculture to recognize and classify plant images and achieved the highest accuracy of 99.58 %.