Weed Detection Using SVMs

Abstract

The major concern in Pakistani agriculture is the reduction of growing weed. This research aims to provide a weed detection tool for future agri-robots. The weed detection tool incorporates the use of machine-learning procedure explicitly implementing Support Vector Machines (SVMs) and blob analysis for the effective classification of crop and weed. Weed revealing is based on characteristic features i.e. red green blue (RGB) components which differentiate soil and plant. Morphological features—centroidand length aid to distinguish shape of crop and weed leaves. Following feature extraction, the positive and negative margins are separated by a hyper-plane. The separating hyper-plane acts as the decision surface. Sample input consists of multiple digital field images of carrot crops. Training samples of seventy two images are taken. Accuracy of the outcomes discloses that SVM and blob analysis attain above 50-95% accuracy.

Нейросети, Глубокое Обучение, Машинное Зрение В Сельском Хозяйстве. Краткий Обзор Для 2021 Года

“…Необходимо использовать стационарный компьютер с достаточной мощностью GPU для выполнения вычислений. В отличие от этих работ, Murawwat et al [15] использует процедуры машинного обучения для реализации опорных векторных машин (SVM) и BLOB объекты для задачи обнаружения урожая с низким энергопотреблением ЦП. Bhongal et al [17] использовали аналогичный стандарт функции и использовали сегментацию и последующее использование нейронной сети для обработки изображений, так же сделали и Амбика и др.…”

Weed detection using machine learning: A systematic literature review

“…Accuracy (ACC) 13 [32], [33], [34], [35], [36], [37], [38], [39][40], [41], [42], [43], [44] Precision 7 [32], [7], [45], [46], [47], [48], [42] F1-score 6 [46], [47], [49], [48], [41], [42] Confusion Matrix 5 [33], [7], [35], [47], [39] Recall (RC) 4 [32], [45], [46], [48] Area under the curve (AUC) 2 [36], [50] Receiver operating characteristic (ROC) 2 [36], [50] Sensitivity 2 [7], [42] Specificity 1 [42] Coefficient of determination (R2) 1 [35] Matthews correlation coefficient (MCC) 1 [46] Means squared error (MSE) 1 [34] Root means squared error (RMSE) 1 [35] 6. RQ3: What are the challenges?…”

Supporting Urban Weed Biosecurity Programs with Remote Sensing

“…• On-ground: Murawaat et al [63] • UAV: Gökto ǧan et al [64] • Government satellite: Xie et al [3] Deep learning (e.g., convolutional neural networks (CNN))…”

“…Some methods, such as maximum likelihood, are well established, while the use of approaches such as CNNs are increasing. Two very commonly used approaches are RF [44,46,48,51,62] and SVM [3,63,64]. SVM, in particular, is able to be effectively applied with limited amounts of calibration data [68], which is an important consideration for detection of rarer species, including small populations of weed species.…”