Groundnuts Leaf Disease Recognition using Neural Network with Progressive Resizing

Abstract

Groundnut is an important oilseed crop in the world, and India is the second-largest producer of groundnuts. This crop is prone to attack by numerous diseases which is one of the most important factors contributing to the loss of productivity and degradation in the quality; both of these finally result in a low agricultural economy. Therefore, it is necessary to find better and more reliable automation solutions to recognize groundnut leaf diseases. In this paper, a deep learning based model with progressive resizing is proposed for groundnut leaf disease recognition and classification tasks. Five major categories of groundnut leaf diseases namely leaf spot, armyworms effect, wilts, yellow leaf, and healthy leaf are considered. The proposed model was trained with and without progressive resizing while it was validated using cross-entropy loss. The first of its kind dataset used for training and validation purposes was manually created from the Saurashtra region of Gujarat state of India. The created dataset was imbalanced in terms of a different number of samples for each category. To handle the imbalanced dataset problem, the extended focal loss function was used. To evaluate the performance of the proposed model, different performance measures including precision, sensitivity, F1-score, and accuracy were applied. The proposed model achieved state-of-the-art accuracy of 96.12%. The model with progressive resizing performed better than the traditional core neural network-based model built on cross-entropy loss.

GNViT- An enhanced image-based groundnut pest classification using Vision Transformer (ViT) model

“…Groundnuts, a vital crop in the Indian agricultural landscape, unfortunately, face numerous diseases. These maladies compromise the crop’s health, negatively impact yield and quality, and have economic ramifications [ 28 ]. While multiple research efforts have harnessed Convolutional Neural Networks (CNNs) to address pest identification challenges in crops, many have limitations in scope and generalizability, often tailored for specific crops and pest varieties [ 29 ].…”

Pest Detection and Classification in Peanut Crops Using CNN, MFO, and EViTA Algorithms

“…The groundnut crop assumes a significant part in the horticultural product ware of Indian economy. In the year 2019 alone, complete groundnut land and creation in India were 3.931 million hectares and 6.862 million MT independently [12], but simultaneously, the average yield is low. Disease attack is a main issue adding to the lack of proficiency, quality, and early downfall of the leaves [13].…”

“…Disease attack is a main issue adding to the lack of proficiency, quality, and early downfall of the leaves [13]. In this way, it is important to move toward fostering a quick and exact groundnut leaf sickness acknowledgment technique to reasonably increment efficiency [12]. The development of most significant field yields like rice, wheat, maize, soybean, and sugarcane are affected because of disease of various bugs and the gather creations are diminished because of various kinds of insects [14].…”

Leaf Diseases Identification and Classification of Self-Collected Dataset on Groundnut Crop using Progressive Convolutional Neural Network (PGCNN)

“…Timely identification of crop leaf diseases is critical in agricultural yield, quality management, and decision-making. [1] This research builds the model based on CNN architecture for groundnut leaf diseases with five categories which was common in Gujarat area with the progressive resizing using cross entropy loss function and achieved the results as 96.12% accuracy. The peanut, scientifically known as arachis hypogaea, it is a herbaceous yearly plant that belongs to the Fabaceae family and is cultivated for its oil, edible nuts, and nutritious snacks.…”