# Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection
## 1 Introduction  
According to the United Nations, the projected population in the year 2050 will be around 10 billion [1]. To sustain this population, global food production needs to increase by 60%. Since potatoes are one of the most widely consumed food products, it is crucial to increase their production. To achieve this, we either need to grow more potato crops or increase the efficiency of existing farmland. The latter option is considered the better choice as it saves space and reduces the effort required to maintain a larger area. More cultivated land would mean higher fertilizer requirements, more labour, and more machinery. In countries like India, where majority of the population is involved in agriculture and related activities, it is vital for farmers to enhance their production capacity. Many farmers have limited land, and some work on other's land on a contractual basis. After rice and wheat, potatoes are the third most widely grown cash crop in India.  
Potato plants can be infected by various diseases such as Early Blight, Late Blight, leaf roll virus, scab, hollow heart, etc. Among these, Late Blight and Early Blight are the most common diseases affecting potato plants. Scientifically, these diseases are known as Phytophthorahora Infestans and Alternaria Solani, respectively. They have drastically impacted crop production over the years, and the quality of remaining crops continues to be affected today. Timely identification of the plant disease can prevent their spread by using suitable treatments, thereby avoiding financial losses. In the past, human inspection was the sole method for detecting plant diseases. However, this approach has several drawbacks: only experts are able to identify the diseases, and human evaluation is prone to error, and the area that humans can cover is limited, etc.  
Technological solutions offer a more robust and effective approach to these problems. Many researchers have already worked on the early detection of plant diseases. As technology advances, even more convenient and simpler solutions can be developed. With the advancement of technologies like Artificial Intelligence (AI) and Computer Vision (CV), disease identification is no longer limited to human inspection alone. Software applications can now instantly identify various disease. Recent developments in agricultural technology have shifted away from the conventional image processing techniques which rely on feature extraction and segmentation for disease detection to more advanced methods of deep learning. In the realm of AI, a new field known as \"deep learning\" has emerged which has shown superior performance, especially compared to the traditional image processing methods. It utilizes kernels, also known as filters, to extract relevant features from images. These extracted features are then used to differentiate among various plant diseases. Each plant disease exhibits a unique pattern on its leaves, distinguishing it from other types of diseases. The combination of traditional image processing methods and deep learning has proven to be an effective approach for identifying and classifying plant diseases.