## 3 Related Works
Despite the fact that this system can identify illness from a plant's leaves, it cannot detect disease from the root. Furthermore, the suggested system employs a large number of IoT devices to run complicated CNN algorithms, resulting in excessive energy consumption and latency. [PERSON] et al. [30] used MATLAB-based image processing to design a less expensive method of automatic irrigation for recognizing rice illnesses and nutritional deficits. The system was built using a Raspberry Pi, a DHT11 temperature and humidity sensor, and solenoid valves. The farmer can keep an eye on the weather. The user or the Pi camera sends images of the leaves to the Pi for analysis with MATLAB. The scientists constructed a database that calculated attributes including contrast, correlation, mean, standard deviation, and entropy, as well as assigning a name to each ailment. The properties of the selected picture were derived from the clusters after the images were segmented into clusters and a Region of Interest (ROI) was chosen [31]. The illnesses are recognized based on the values of parameters that match the values in the database. The farmer will be fully informed on strategies to save the plant from the disease's effects via the feedback process. This IoT-based intelligent system with nutrient and disease analysis is smarter and optimized for irrigation, lowering overheads and allowing for more effective water usage, which aids in water conservation. Furthermore, the automatic diagnosis of nutrient insufficiency and disease analysis will allow farmers to boost their yield with less effort. The performance of CNN in object recognition and image classification has been on the rise, large cumbersome models such as Resnet50, Alexnet are achieving state-of-the-art accuracies at the cost of computation. The accuracy of these models increases as their parameters are increasing exponentially. This performance along with the simplicity of CNN's paved the way for ML to be applied in many domains where image classification can solve the problem, such as a disease diagnosis from medical imaging [32]. Previous works such as [PERSON] et al. [33] use traditional machine learning algorithms to solve the problem of image classification, the approach was based on hand-engineered feature extraction methods, and then a learning algorithm or a classifier is applied to these extracted features. The process is both tedious and troublesome because when the dataset changes significantly, then the features have to be extracted again. The performance of these traditional methods depends heavily on the predefined features. This problem occurs in almost all the traditional approaches of image classification using computer vision, the features are extracted and manually hand-engineered followed by image enhancement techniques and all sorts of complex and labor-intensive methodologies. However, with the advent of a convolutional neural network, the narrative was changed completely. Hand-engineered feature extraction is thus replaced by automatic features extraction using convolutions. The earliest work on the use of CNN to detect plant diseases was seen in the work of [34; 35; 36], who proposed a deep-learning-based plant disease detection model. In their work, the authors demonstrated how CNN can be used to replace the traditional method of plant diseases detection of using hand-engineered features selected. They indicated that that process can be eliminated by convolution operations. The system achieved a remarkable accuracy of 99.7% using transfer learning; specifically, the model used pre-trained models Alexnet and Googlenet for better performance, but the models were known to be computationally expensive as a result of their complex architecture. Nevertheless, neither the computational requirements nor the power consumption of the model was reported. Similarly, [PERSON] et al. [8] proposed a deep learning model of plant diseases detection for smart hydroponics.